2000 character limit reached
Experiential Explanations for Reinforcement Learning (2210.04723v4)
Published 10 Oct 2022 in cs.AI and cs.HC
Abstract: Reinforcement Learning (RL) systems can be complex and non-interpretable, making it challenging for non-AI experts to understand or intervene in their decisions. This is due in part to the sequential nature of RL in which actions are chosen because of future rewards. However, RL agents discard the qualitative features of their training, making it difficult to recover user-understandable information for "why" an action is chosen. We propose a technique, Experiential Explanations, to generate counterfactual explanations by training influence predictors along with the RL policy. Influence predictors are models that learn how sources of reward affect the agent in different states, thus restoring information about how the policy reflects the environment. A human evaluation study revealed that participants presented with experiential explanations were better able to correctly guess what an agent would do than those presented with other standard types of explanation. Participants also found that experiential explanations are more understandable, satisfying, complete, useful, and accurate. The qualitative analysis provides insights into the factors of experiential explanations that are most useful.
- Zelvelder, A.E., Westberg, M., Främling, K.: Assessing explainability in reinforcement learning. In: International Workshop on Explainable, Transparent Autonomous Agents and Multi-Agent Systems, pp. 223–240 (2021). Springer Anjomshoae et al. [2019] Anjomshoae, S., Najjar, A., Calvaresi, D., Främling, K.: Explainable Agents and Robots: Results from a Systematic Literature Review. In: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems. AAMAS ’19, pp. 1078–1088. International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC (2019) Alharin et al. [2020] Alharin, A., Doan, T.-N., Sartipi, M.: Reinforcement learning interpretation methods: A survey. IEEE Access 8, 171058–171077 (2020) Wells and Bednarz [2021] Wells, L., Bednarz, T.: Explainable AI and Reinforcement Learning—A Systematic Review of Current Approaches and Trends. Frontiers in Artificial Intelligence 4 (2021). Accessed 2022-10-01 Heuillet et al. [2021] Heuillet, A., Couthouis, F., Díaz-Rodríguez, N.: Explainability in deep reinforcement learning. Knowledge-Based Systems 214, 106685 (2021) https://doi.org/10.1016/j.knosys.2020.106685 . Accessed 2022-09-30 Puiutta and Veith [2020] Puiutta, E., Veith, E.M.S.P.: Explainable Reinforcement Learning: A Survey. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science, pp. 77–95. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57321-8_5 Milani et al. [2023] Milani, S., Topin, N., Veloso, M., Fang, F.: Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys (2023) https://doi.org/10.1145/3616864 Chakraborti et al. [2020] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Anjomshoae, S., Najjar, A., Calvaresi, D., Främling, K.: Explainable Agents and Robots: Results from a Systematic Literature Review. In: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems. AAMAS ’19, pp. 1078–1088. International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC (2019) Alharin et al. [2020] Alharin, A., Doan, T.-N., Sartipi, M.: Reinforcement learning interpretation methods: A survey. IEEE Access 8, 171058–171077 (2020) Wells and Bednarz [2021] Wells, L., Bednarz, T.: Explainable AI and Reinforcement Learning—A Systematic Review of Current Approaches and Trends. Frontiers in Artificial Intelligence 4 (2021). Accessed 2022-10-01 Heuillet et al. [2021] Heuillet, A., Couthouis, F., Díaz-Rodríguez, N.: Explainability in deep reinforcement learning. Knowledge-Based Systems 214, 106685 (2021) https://doi.org/10.1016/j.knosys.2020.106685 . Accessed 2022-09-30 Puiutta and Veith [2020] Puiutta, E., Veith, E.M.S.P.: Explainable Reinforcement Learning: A Survey. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science, pp. 77–95. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57321-8_5 Milani et al. [2023] Milani, S., Topin, N., Veloso, M., Fang, F.: Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys (2023) https://doi.org/10.1145/3616864 Chakraborti et al. [2020] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Alharin, A., Doan, T.-N., Sartipi, M.: Reinforcement learning interpretation methods: A survey. IEEE Access 8, 171058–171077 (2020) Wells and Bednarz [2021] Wells, L., Bednarz, T.: Explainable AI and Reinforcement Learning—A Systematic Review of Current Approaches and Trends. Frontiers in Artificial Intelligence 4 (2021). Accessed 2022-10-01 Heuillet et al. [2021] Heuillet, A., Couthouis, F., Díaz-Rodríguez, N.: Explainability in deep reinforcement learning. Knowledge-Based Systems 214, 106685 (2021) https://doi.org/10.1016/j.knosys.2020.106685 . Accessed 2022-09-30 Puiutta and Veith [2020] Puiutta, E., Veith, E.M.S.P.: Explainable Reinforcement Learning: A Survey. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science, pp. 77–95. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57321-8_5 Milani et al. [2023] Milani, S., Topin, N., Veloso, M., Fang, F.: Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys (2023) https://doi.org/10.1145/3616864 Chakraborti et al. [2020] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Wells, L., Bednarz, T.: Explainable AI and Reinforcement Learning—A Systematic Review of Current Approaches and Trends. Frontiers in Artificial Intelligence 4 (2021). Accessed 2022-10-01 Heuillet et al. [2021] Heuillet, A., Couthouis, F., Díaz-Rodríguez, N.: Explainability in deep reinforcement learning. Knowledge-Based Systems 214, 106685 (2021) https://doi.org/10.1016/j.knosys.2020.106685 . Accessed 2022-09-30 Puiutta and Veith [2020] Puiutta, E., Veith, E.M.S.P.: Explainable Reinforcement Learning: A Survey. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science, pp. 77–95. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57321-8_5 Milani et al. [2023] Milani, S., Topin, N., Veloso, M., Fang, F.: Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys (2023) https://doi.org/10.1145/3616864 Chakraborti et al. [2020] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Heuillet, A., Couthouis, F., Díaz-Rodríguez, N.: Explainability in deep reinforcement learning. Knowledge-Based Systems 214, 106685 (2021) https://doi.org/10.1016/j.knosys.2020.106685 . Accessed 2022-09-30 Puiutta and Veith [2020] Puiutta, E., Veith, E.M.S.P.: Explainable Reinforcement Learning: A Survey. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science, pp. 77–95. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57321-8_5 Milani et al. [2023] Milani, S., Topin, N., Veloso, M., Fang, F.: Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys (2023) https://doi.org/10.1145/3616864 Chakraborti et al. [2020] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Puiutta, E., Veith, E.M.S.P.: Explainable Reinforcement Learning: A Survey. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science, pp. 77–95. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57321-8_5 Milani et al. [2023] Milani, S., Topin, N., Veloso, M., Fang, F.: Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys (2023) https://doi.org/10.1145/3616864 Chakraborti et al. [2020] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Milani, S., Topin, N., Veloso, M., Fang, F.: Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys (2023) https://doi.org/10.1145/3616864 Chakraborti et al. [2020] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Anjomshoae, S., Najjar, A., Calvaresi, D., Främling, K.: Explainable Agents and Robots: Results from a Systematic Literature Review. In: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems. AAMAS ’19, pp. 1078–1088. International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC (2019) Alharin et al. [2020] Alharin, A., Doan, T.-N., Sartipi, M.: Reinforcement learning interpretation methods: A survey. IEEE Access 8, 171058–171077 (2020) Wells and Bednarz [2021] Wells, L., Bednarz, T.: Explainable AI and Reinforcement Learning—A Systematic Review of Current Approaches and Trends. Frontiers in Artificial Intelligence 4 (2021). Accessed 2022-10-01 Heuillet et al. [2021] Heuillet, A., Couthouis, F., Díaz-Rodríguez, N.: Explainability in deep reinforcement learning. Knowledge-Based Systems 214, 106685 (2021) https://doi.org/10.1016/j.knosys.2020.106685 . Accessed 2022-09-30 Puiutta and Veith [2020] Puiutta, E., Veith, E.M.S.P.: Explainable Reinforcement Learning: A Survey. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science, pp. 77–95. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57321-8_5 Milani et al. [2023] Milani, S., Topin, N., Veloso, M., Fang, F.: Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys (2023) https://doi.org/10.1145/3616864 Chakraborti et al. [2020] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Alharin, A., Doan, T.-N., Sartipi, M.: Reinforcement learning interpretation methods: A survey. IEEE Access 8, 171058–171077 (2020) Wells and Bednarz [2021] Wells, L., Bednarz, T.: Explainable AI and Reinforcement Learning—A Systematic Review of Current Approaches and Trends. Frontiers in Artificial Intelligence 4 (2021). Accessed 2022-10-01 Heuillet et al. [2021] Heuillet, A., Couthouis, F., Díaz-Rodríguez, N.: Explainability in deep reinforcement learning. Knowledge-Based Systems 214, 106685 (2021) https://doi.org/10.1016/j.knosys.2020.106685 . Accessed 2022-09-30 Puiutta and Veith [2020] Puiutta, E., Veith, E.M.S.P.: Explainable Reinforcement Learning: A Survey. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science, pp. 77–95. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57321-8_5 Milani et al. [2023] Milani, S., Topin, N., Veloso, M., Fang, F.: Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys (2023) https://doi.org/10.1145/3616864 Chakraborti et al. [2020] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Wells, L., Bednarz, T.: Explainable AI and Reinforcement Learning—A Systematic Review of Current Approaches and Trends. Frontiers in Artificial Intelligence 4 (2021). Accessed 2022-10-01 Heuillet et al. [2021] Heuillet, A., Couthouis, F., Díaz-Rodríguez, N.: Explainability in deep reinforcement learning. Knowledge-Based Systems 214, 106685 (2021) https://doi.org/10.1016/j.knosys.2020.106685 . Accessed 2022-09-30 Puiutta and Veith [2020] Puiutta, E., Veith, E.M.S.P.: Explainable Reinforcement Learning: A Survey. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science, pp. 77–95. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57321-8_5 Milani et al. [2023] Milani, S., Topin, N., Veloso, M., Fang, F.: Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys (2023) https://doi.org/10.1145/3616864 Chakraborti et al. [2020] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Heuillet, A., Couthouis, F., Díaz-Rodríguez, N.: Explainability in deep reinforcement learning. Knowledge-Based Systems 214, 106685 (2021) https://doi.org/10.1016/j.knosys.2020.106685 . Accessed 2022-09-30 Puiutta and Veith [2020] Puiutta, E., Veith, E.M.S.P.: Explainable Reinforcement Learning: A Survey. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science, pp. 77–95. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57321-8_5 Milani et al. [2023] Milani, S., Topin, N., Veloso, M., Fang, F.: Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys (2023) https://doi.org/10.1145/3616864 Chakraborti et al. [2020] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Puiutta, E., Veith, E.M.S.P.: Explainable Reinforcement Learning: A Survey. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science, pp. 77–95. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57321-8_5 Milani et al. [2023] Milani, S., Topin, N., Veloso, M., Fang, F.: Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys (2023) https://doi.org/10.1145/3616864 Chakraborti et al. [2020] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Milani, S., Topin, N., Veloso, M., Fang, F.: Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys (2023) https://doi.org/10.1145/3616864 Chakraborti et al. [2020] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Alharin, A., Doan, T.-N., Sartipi, M.: Reinforcement learning interpretation methods: A survey. IEEE Access 8, 171058–171077 (2020) Wells and Bednarz [2021] Wells, L., Bednarz, T.: Explainable AI and Reinforcement Learning—A Systematic Review of Current Approaches and Trends. Frontiers in Artificial Intelligence 4 (2021). Accessed 2022-10-01 Heuillet et al. [2021] Heuillet, A., Couthouis, F., Díaz-Rodríguez, N.: Explainability in deep reinforcement learning. Knowledge-Based Systems 214, 106685 (2021) https://doi.org/10.1016/j.knosys.2020.106685 . Accessed 2022-09-30 Puiutta and Veith [2020] Puiutta, E., Veith, E.M.S.P.: Explainable Reinforcement Learning: A Survey. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science, pp. 77–95. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57321-8_5 Milani et al. [2023] Milani, S., Topin, N., Veloso, M., Fang, F.: Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys (2023) https://doi.org/10.1145/3616864 Chakraborti et al. [2020] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Wells, L., Bednarz, T.: Explainable AI and Reinforcement Learning—A Systematic Review of Current Approaches and Trends. Frontiers in Artificial Intelligence 4 (2021). Accessed 2022-10-01 Heuillet et al. [2021] Heuillet, A., Couthouis, F., Díaz-Rodríguez, N.: Explainability in deep reinforcement learning. Knowledge-Based Systems 214, 106685 (2021) https://doi.org/10.1016/j.knosys.2020.106685 . Accessed 2022-09-30 Puiutta and Veith [2020] Puiutta, E., Veith, E.M.S.P.: Explainable Reinforcement Learning: A Survey. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science, pp. 77–95. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57321-8_5 Milani et al. [2023] Milani, S., Topin, N., Veloso, M., Fang, F.: Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys (2023) https://doi.org/10.1145/3616864 Chakraborti et al. [2020] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Heuillet, A., Couthouis, F., Díaz-Rodríguez, N.: Explainability in deep reinforcement learning. Knowledge-Based Systems 214, 106685 (2021) https://doi.org/10.1016/j.knosys.2020.106685 . Accessed 2022-09-30 Puiutta and Veith [2020] Puiutta, E., Veith, E.M.S.P.: Explainable Reinforcement Learning: A Survey. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science, pp. 77–95. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57321-8_5 Milani et al. [2023] Milani, S., Topin, N., Veloso, M., Fang, F.: Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys (2023) https://doi.org/10.1145/3616864 Chakraborti et al. [2020] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Puiutta, E., Veith, E.M.S.P.: Explainable Reinforcement Learning: A Survey. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science, pp. 77–95. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57321-8_5 Milani et al. [2023] Milani, S., Topin, N., Veloso, M., Fang, F.: Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys (2023) https://doi.org/10.1145/3616864 Chakraborti et al. [2020] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Milani, S., Topin, N., Veloso, M., Fang, F.: Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys (2023) https://doi.org/10.1145/3616864 Chakraborti et al. [2020] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Wells, L., Bednarz, T.: Explainable AI and Reinforcement Learning—A Systematic Review of Current Approaches and Trends. Frontiers in Artificial Intelligence 4 (2021). Accessed 2022-10-01 Heuillet et al. [2021] Heuillet, A., Couthouis, F., Díaz-Rodríguez, N.: Explainability in deep reinforcement learning. Knowledge-Based Systems 214, 106685 (2021) https://doi.org/10.1016/j.knosys.2020.106685 . Accessed 2022-09-30 Puiutta and Veith [2020] Puiutta, E., Veith, E.M.S.P.: Explainable Reinforcement Learning: A Survey. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science, pp. 77–95. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57321-8_5 Milani et al. [2023] Milani, S., Topin, N., Veloso, M., Fang, F.: Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys (2023) https://doi.org/10.1145/3616864 Chakraborti et al. [2020] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Heuillet, A., Couthouis, F., Díaz-Rodríguez, N.: Explainability in deep reinforcement learning. Knowledge-Based Systems 214, 106685 (2021) https://doi.org/10.1016/j.knosys.2020.106685 . Accessed 2022-09-30 Puiutta and Veith [2020] Puiutta, E., Veith, E.M.S.P.: Explainable Reinforcement Learning: A Survey. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science, pp. 77–95. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57321-8_5 Milani et al. [2023] Milani, S., Topin, N., Veloso, M., Fang, F.: Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys (2023) https://doi.org/10.1145/3616864 Chakraborti et al. [2020] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Puiutta, E., Veith, E.M.S.P.: Explainable Reinforcement Learning: A Survey. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science, pp. 77–95. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57321-8_5 Milani et al. [2023] Milani, S., Topin, N., Veloso, M., Fang, F.: Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys (2023) https://doi.org/10.1145/3616864 Chakraborti et al. [2020] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Milani, S., Topin, N., Veloso, M., Fang, F.: Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys (2023) https://doi.org/10.1145/3616864 Chakraborti et al. [2020] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Heuillet, A., Couthouis, F., Díaz-Rodríguez, N.: Explainability in deep reinforcement learning. Knowledge-Based Systems 214, 106685 (2021) https://doi.org/10.1016/j.knosys.2020.106685 . Accessed 2022-09-30 Puiutta and Veith [2020] Puiutta, E., Veith, E.M.S.P.: Explainable Reinforcement Learning: A Survey. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science, pp. 77–95. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57321-8_5 Milani et al. [2023] Milani, S., Topin, N., Veloso, M., Fang, F.: Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys (2023) https://doi.org/10.1145/3616864 Chakraborti et al. [2020] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Puiutta, E., Veith, E.M.S.P.: Explainable Reinforcement Learning: A Survey. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science, pp. 77–95. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57321-8_5 Milani et al. [2023] Milani, S., Topin, N., Veloso, M., Fang, F.: Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys (2023) https://doi.org/10.1145/3616864 Chakraborti et al. [2020] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Milani, S., Topin, N., Veloso, M., Fang, F.: Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys (2023) https://doi.org/10.1145/3616864 Chakraborti et al. [2020] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Puiutta, E., Veith, E.M.S.P.: Explainable Reinforcement Learning: A Survey. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science, pp. 77–95. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57321-8_5 Milani et al. [2023] Milani, S., Topin, N., Veloso, M., Fang, F.: Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys (2023) https://doi.org/10.1145/3616864 Chakraborti et al. [2020] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Milani, S., Topin, N., Veloso, M., Fang, F.: Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys (2023) https://doi.org/10.1145/3616864 Chakraborti et al. [2020] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Milani, S., Topin, N., Veloso, M., Fang, F.: Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys (2023) https://doi.org/10.1145/3616864 Chakraborti et al. [2020] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Chakraborti, T., Sreedharan, S., Kambhampati, S.: The Emerging Landscape of Explainable Automated Planning & Decision Making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4803–4811. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020). https://doi.org/10.24963/ijcai.2020/669 . https://www.ijcai.org/proceedings/2020/669 Accessed 2022-03-11 Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA (2018) Chakraborti et al. [2021] Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4803–4811 (2021) Ehsan et al. [2019] Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019) Das and Chernova [2020] Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Das, D., Chernova, S.: Leveraging rationales to improve human task performance. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 510–518 (2020) Juozapaitis et al. [2019] Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition. In: IJCAI/ECAI Workshop on Explainable Artificial Intelligence (2019) Anderson et al. [2019] Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Fern, A., Burnett, M.: Explaining reinforcement learning to mere mortals: An empirical study. CoRR abs/1903.09708 (2019) Septon et al. [2023] Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Septon, Y., Huber, T., André, E., Amir, O.: Integrating policy summaries with reward decomposition for explaining reinforcement learning agents. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 320–332 (2023). Springer Frost et al. [2022] Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Frost, J., Watkins, O., Weiner, E., Abbeel, P., Darrell, T., Plummer, B., Saenko, K.: Explaining reinforcement learning policies through counterfactual trajectories. ICML Workshop on Human in the Loop Learning (HILL) (2022) van der Waa et al. [2018] Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Waa, J., Diggelen, J., Bosch, K.v.d., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences. arXiv preprint arXiv:1807.08706 (2018) Madumal et al. [2020] Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2493–2500 (2020) Sreedharan et al. [2022] Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Sreedharan, S., Soni, U., Verma, M., Srivastava, S., Kambhampati, S.: Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=o-1v9hdSult Olson et al. [2021] Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Olson, M.L., Khanna, R., Neal, L., Li, F., Wong, W.-K.: Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295, 103455 (2021) Huber et al. [2023] Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Huber, T., Demmler, M., Mertes, S., Olson, M.L., André, E.: Ganterfactual-rl: Understanding reinforcement learning agents’ strategies through visual counterfactual explanations. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp. 1097–1106 (2023) Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016). PMLR Ng et al. [1999] Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: Theory and application to reward shaping. In: Icml, vol. 99, pp. 278–287 (1999). Citeseer Hafner et al. [2020] Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Hafner, D., Lillicrap, T., Norouzi, M., Ba, J.: Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193 (2020) Dazeley et al. [2021] Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., Cruz, F.: Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299, 103525 (2021) Chevalier-Boisvert et al. [2018] Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Chevalier-Boisvert, M., Willems, L., Pal, S.: Minimalistic Gridworld Environment for OpenAI Gym. GitHub (2018). https://github.com/maximecb/gym-minigrid Willems [2018] Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Willems, L.: RL Starter Files for MiniGrid (2018). https://github.com/lcswillems/rl-starter-files/tree/4205e05b7905fec16519bc0802596673d86af018 Accessed 2022-09-28 Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) Hoffman et al. [2023] Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable ai: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-ai performance. Frontiers in Computer Science 5, 1096257 (2023) Kiger and Varpio [2020] Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Kiger, M.E., Varpio, L.: Thematic analysis of qualitative data: Amee guide no. 131. Medical Teacher 42, 846–854 (2020) Miles et al. [2020] Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Miles, M.B., Huberman, A.M., Saldaña, J.: Qualitative Data Analysis : a Methods Sourcebook, Fourth edition edn. SAGE Los Angeles, Los Angeles (2020) Zhou et al. [2021] Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) Hafner [2021] Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Hafner, D.: Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780 (2021) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) Chan et al. [1995] Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Chan, W.T., Chow, Y.K., Liu, L.F.: Neural network: An alternative to pile driving formulas. Computers and Geotechnics 17(2), 135–156 (1995) https://doi.org/10.1016/0266-352X(95)93866-H Stanić et al. [2022] Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022) Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Stanić, A., Tang, Y., Ha, D., Schmidhuber, J.: Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (2022)
- Amal Alabdulkarim (6 papers)
- Madhuri Singh (2 papers)
- Gennie Mansi (5 papers)
- Kaely Hall (1 paper)
- Mark O. Riedl (57 papers)