Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
129 tokens/sec
GPT-4o
28 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents (2401.05821v4)

Published 11 Jan 2024 in cs.LG and cs.SC

Abstract: Goal misalignment, reward sparsity and difficult credit assignment are only a few of the many issues that make it difficult for deep reinforcement learning (RL) agents to learn optimal policies. Unfortunately, the black-box nature of deep neural networks impedes the inclusion of domain experts for inspecting the model and revising suboptimal policies. To this end, we introduce Successive Concept Bottleneck Agents (SCoBots), that integrate consecutive concept bottleneck (CB) layers. In contrast to current CB models, SCoBots do not just represent concepts as properties of individual objects, but also as relations between objects which is crucial for many RL tasks. Our experimental results provide evidence of SCoBots' competitive performances, but also of their potential for domain experts to understand and regularize their behavior. Among other things, SCoBots enabled us to identify a previously unknown misalignment problem in the iconic video game, Pong, and resolve it. Overall, SCoBots thus result in more human-aligned RL agents. Our code is available at https://github.com/k4ntz/SCoBots .

Definition Search Book Streamline Icon: https://streamlinehq.com
References (77)
  1. Hindsight experience replay. Advances in neural information processing systems, 2017.
  2. Rationalization through concepts. ArXiv, 2021.
  3. Value alignment or misalignment – what will keep systems accountable? In AAAI Workshop on AI, Ethics, and Society, 2017.
  4. The option-critic architecture. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA, 2017.
  5. Debiasing concept bottleneck models with instrumental variables. ArXiv, 2020.
  6. The arcade learning environment: An evaluation platform for general agents (extended abstract). In International Joint Conference on Artificial Intelligence, 2012.
  7. Concept-level debugging of part-prototype networks. In International Conference on Learning Representations (ICLR). OpenReview.net, 2023.
  8. A gradient-based split criterion for highly accurate and transparent model trees. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019, 2019.
  9. A comparative study of faithfulness metrics for model interpretability methods. In Conference of the Association for Computational Linguistics (ACL), pp.  5029–5038. Association for Computational Linguistics, 2022.
  10. Interactive concept bottleneck models. ArXiv, 2022.
  11. Quantifying generalization in reinforcement learning. In Chaudhuri, K. and Salakhutdinov, R. (eds.), Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 2019.
  12. Playing atari with six neurons (extended abstract). In Bessiere, C. (ed.), Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, 2020.
  13. Towards symbolic reinforcement learning with common sense, 2018.
  14. Levels of explainable artificial intelligence for human-aligned conversational explanations. Artif. Intell., 2021.
  15. Explainable reinforcement learning for broad-xai: a conceptual framework and survey. Neural Computing and Applications, 2022.
  16. Adaptive rational activations to boost deep reinforcement learning. 2021.
  17. Ocatari: Object-centric atari 2600 reinforcement learning environments. ArXiv, 2023a.
  18. Interpretable and explainable logical policies via neurally guided symbolic abstraction. ArXiv, 2023b.
  19. Boosting object representation learning via motion and object continuity. In Koutra, D., Plant, C., Rodriguez, M. G., Baralis, E., and Bonchi, F. (eds.), European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), volume 14172 of Lecture Notes in Computer Science, pp.  610–628. Springer, 2023c.
  20. ERASER: A benchmark to evaluate rationalized NLP models. In Conference of the Association for Computational Linguistics (ACL), pp.  4443–4458. Association for Computational Linguistics, 2020.
  21. Goal misgeneralization in deep reinforcement learning. In Chaudhuri, K., Jegelka, S., Song, L., Szepesvári, C., Niu, G., and Sabato, S. (eds.), International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA, 2022.
  22. Shortcut learning in deep neural networks. Nature Machine Intelligence, 2020.
  23. Concept-based understanding of emergent multi-agent behavior. In Deep Reinforcement Learning Workshop NeurIPS 2022, 2022.
  24. Relative behavioral attributes: Filling the gap between symbolic goal specification and reward learning from human preferences. In International Conference on Learning Representations (ICLR). OpenReview.net, 2023.
  25. A survey of methods for explaining black box models. ACM Computing Surveys, 51(5):93:1–93:42, 2019.
  26. Deep reinforcement learning that matters. In AAAI Conference on Artificial Intelligence, 2017.
  27. A benchmark for interpretability methods in deep neural networks. In Conference on Neural Information Processing Systems (NeurIPS 2019), pp.  9734–9745, 2019.
  28. Ai safety via debate. ArXiv, 2018.
  29. Visual explanation using attention mechanism in actor-critic-based deep reinforcement learning. 2021 International Joint Conference on Neural Networks (IJCNN), 2021.
  30. Unsupervised curricula for visual meta-reinforcement learning. ArXiv, 2019.
  31. Model-based reinforcement learning for atari. ArXiv, 2019.
  32. Symbols as a lingua franca for bridging human-ai chasm for explainable and advisable ai systems. In AAAI Conference on Artificial Intelligence, 2021.
  33. Objective robustness in deep reinforcement learning, 2021.
  34. Concept bottleneck models. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, 2020.
  35. Explainability in reinforcement learning: perspective and position. ArXiv, 2022.
  36. Attribute and simile classifiers for face verification. 2009 IEEE 12th International Conference on Computer Vision, 2009.
  37. Learning interpretable concept-based models with human feedback. ArXiv, 2020.
  38. Learning to detect unseen object classes by between-class attribute transfer. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009.
  39. Unmasking clever hans predictors and assessing what machines really learn. Nature communications, 10(1):1096, 2019.
  40. SPACE: unsupervised object-oriented scene representation via spatial attention and decomposition. In International Conference on Learning Representations, 2020.
  41. Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents (extended abstract). In Lang, J. (ed.), Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden. ijcai.org, 2018.
  42. Glancenets: Interpretable, leak-proof concept-based models. In Advances in Neural Information Processing (NeurIPS), 2022.
  43. Neuro-symbolic reasoning shortcuts: Mitigation strategies and their limitations. In International Workshop on Neural-Symbolic Learning and Reasoning, volume 3432 of CEUR Workshop Proceedings, pp. 162–166, 2023.
  44. Counterfactual credit assignment in model-free reinforcement learning. In Proceedings of the 38th International Conference on Machine Learning (ICML), 2021.
  45. Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys, 2023.
  46. Playing atari with deep reinforcement learning. ArXiv, 2013.
  47. Human-level control through deep reinforcement learning. Nature, 2015.
  48. Training value-aligned reinforcement learning agents using a normative prior. ArXiv, 2021.
  49. Policy invariance under reward transformations: Theory and application to reward shaping. In International Conference on Machine Learning, 1999.
  50. Ngo, R. The alignment problem from a deep learning perspective. ArXiv, 2022.
  51. Neat for large-scale reinforcement learning through evolutionary feature learning and policy gradient search. Proceedings of the Genetic and Evolutionary Computation Conference, 2018.
  52. A survey on explainable reinforcement learning: Concepts, algorithms, challenges. ArXiv, 2022.
  53. Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research, 22(268), 2021.
  54. Synthetic returns for long-term credit assignment. ArXiv, 2021.
  55. Explainable deep learning: A field guide for the uninitiated. Journal of Artificial Intelligence Research, 73:329–396, 2022.
  56. You only look once: Unified, real-time object detection. In Conference on Computer Vision and Pattern Recognition, CVPR 2016, 2016.
  57. Can wikipedia help offline reinforcement learning? ArXiv, 2022.
  58. Explainability via causal self-talk. 2022.
  59. Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities. Knowledge-Based Systems, 263:110273, 2023.
  60. Concept bottleneck model with additional unsupervised concepts. IEEE Access, 10:41758–41765, 2022.
  61. Making deep neural networks right for the right scientific reasons by interacting with their explanations. Nature Machine Intelligence, 2(8):476–486, 2020.
  62. Proximal policy optimization algorithms. ArXiv, 2017.
  63. Curl: Contrastive unsupervised representations for reinforcement learning. ArXiv, 2020.
  64. Right for the right concept: Revising neuro-symbolic concepts by interacting with their explanations. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2021.
  65. Interactive disentanglement: Learning concepts by interacting with their prototype representations. In Conference on Computer Vision and Pattern Recognition, (CVPR), pp.  10307–10318, 2022.
  66. Learning to intervene on concept bottlenecks. ArXiv, 2023.
  67. Leveraging explanations in interactive machine learning: An overview. Frontiers in Artificial Intelligence, 2023.
  68. Touzet, C. F. Neural reinforcement learning for behaviour synthesis. Robotics Auton. Syst., 1997.
  69. Deep reinforcement learning with double q-learning. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA, 2016.
  70. Vouros, G. A. Explainable deep reinforcement learning: State of the art and challenges. ACM Computing Surveys, 2022.
  71. Visual rationalizations in deep reinforcement learning for atari games. In BNCAI, 2018.
  72. Read and reap the rewards: Learning to play atari with the help of instruction manuals. ArXiv, 2023.
  73. Evolutionary reinforcement learning via cooperative coevolutionary negatively arxivelated search. Swarm and Evolutionary Computation, 2022.
  74. Concept learning for interpretable multi-agent reinforcement learning. ArXiv, 2023.
  75. Efficient decompositional rule extraction for deep neural networks. CoRR, 2021.
  76. Vision-based robot navigation through combining unsupervised learning and hierarchical reinforcement learning. Sensors (Basel, Switzerland), 2019.
  77. Çağlar Aytekin. Neural networks are decision trees. ArXiv, 2022.
Citations (11)
View on Semantic Scholar

Summary

  • The paper introduces SCoBots, a novel framework that employs successive concept bottlenecks to improve the interpretability of reinforcement learning agents.
  • The paper demonstrates competitive performance on Atari benchmarks while providing human-understandable explanations for the agents' decisions.
  • The paper highlights the potential for expert intervention to correct goal misalignment and enhance the reliability of RL outcomes.

Introduction to Successive Concept Bottlenecks Agents

Reinforcement learning (RL) is a critical area of research within AI, which aims to develop agents capable of learning optimal behaviors through trial and error in dynamic environments. RL, particularly in its deep learning form, often deals with obstacles including sparse rewards and goal misalignment, which hamper the learning of policies that can be generalized beyond the specific training scenarios. These hurdles are exacerbated by the difficulty of interpreting the decision-making process of deep RL agents, limiting the ability of domain experts to step in and guide or correct these agents. Addressing this gap, the newly introduced Successive Concept Bottlenecks Agents (SCoBots) model aims to enhance the transparency of deep RL agents through a novel approach incorporating interpretable concept layers.

Transparent Decision-Making in RL

SCoBots are designed to open up the traditionally opaque decision-making process of RL agents, providing transparency at multiple levels of the reasoning process. This multifaceted visibility extends from basic object properties to relational concepts and, ultimately, to the decisions made by the agent. The interpretability afforded by SCoBots is not merely theoretical; experimental results on standard benchmark Atari games demonstrate that these agents are capable of learning competitive policies comparable to deep RL agents. Perhaps most intriguingly, SCoBots can offer valuable insights and explanations for their decisions, which, in turn, enable domain experts to identify and resolve issues such as goal misalignment using human-understandable concepts.

Innovative Approach to Bottleneck Integration

The integration of concept bottleneck layers within the SCoBots framework is innovative, especially in the way it combines successive layers to form a more comprehensive and interpretable decision-making process. Each bottleneck layer incorporates concepts from the previous layers, allowing for a cumulative buildup of relational knowledge that supports the RL agent's action selection. This tiered approach also facilitates human intervention, enabling the pruning of irrelevant concepts or the addition of new ones as needed. These successive bottlenecks are closer to human reasoning, helping bridge the gap between AI decision-making processes and human expertise.

Practical Impact and Future Applications

SCoBots offer an exciting glimpse into the future of RL, where agents operate with increased transparency and interpretability. The potential for domain experts to seamlessly interact with and guide these agents opens up opportunities for a more natural collaboration between humans and AI, improving trust and reliability. The ability to identify and mitigate goal misalignment and other RL-specific issues is crucial for deploying RL agents in real-world scenarios that require adherence to safety and ethical standards. SCoBots represent an essential step on the path toward developing RL agents that not only understand the tasks at hand but can also align their learning processes with the understanding and intentions of human users. Future research may ingrain even more human-like decision mechanisms, like attention or language understanding, further enhancing the alignment of RL agents with human expectations and ethical considerations.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown
Github Logo Streamline Icon: https://streamlinehq.com

GitHub

  1. GitHub - k4ntz/SCoBots: Explainable Modular Reinforcement Learning (from images) (11 stars)