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Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity (2404.07266v2)

Published 10 Apr 2024 in cs.LG

Abstract: We study the problem of online sequential decision-making given auxiliary demonstrations from experts who made their decisions based on unobserved contextual information. These demonstrations can be viewed as solving related but slightly different problems than what the learner faces. This setting arises in many application domains, such as self-driving cars, healthcare, and finance, where expert demonstrations are made using contextual information, which is not recorded in the data available to the learning agent. We model the problem as zero-shot meta-reinforcement learning with an unknown distribution over the unobserved contextual variables and a Bayesian regret minimization objective, where the unobserved variables are encoded as parameters with an unknown prior. We propose the Experts-as-Priors algorithm (ExPerior), an empirical Bayes approach that utilizes expert data to establish an informative prior distribution over the learner's decision-making problem. This prior distribution enables the application of any Bayesian approach for online decision-making, such as posterior sampling. We demonstrate that our strategy surpasses existing behaviour cloning, online, and online-offline baselines for multi-armed bandits, Markov decision processes (MDPs), and partially observable MDPs, showcasing the broad reach and utility of ExPerior in using expert demonstrations across different decision-making setups.

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References (54)
  1. Efficient online reinforcement learning with offline data. arXiv preprint arXiv:2302.02948.
  2. A survey of meta-reinforcement learning. arXiv preprint arXiv:2301.08028.
  3. Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders. In Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, pages 1999–2007. PMLR. ISSN: 2640-3498.
  4. Techniques of variational analysis.
  5. Nonparametric Discrete Choice Models With Unobserved Heterogeneity. Journal of Business & Economic Statistics, 28(2):291–307. Publisher: [American Statistical Association, Taylor & Francis, Ltd.].
  6. Scaling data-driven robotics with reward sketching and batch reinforcement learning. arXiv preprint arXiv:1909.12200.
  7. Empirical bayes: Past, present and future. Journal of the American Statistical Association, 95(452):1286–1289.
  8. Meta-learning with stochastic linear bandits. In International Conference on Machine Learning, pages 1360–1370. PMLR.
  9. Multi-task and meta-learning with sparse linear bandits. In Uncertainty in Artificial Intelligence, pages 1692–1702. PMLR.
  10. Data-driven planning via imitation learning. The International Journal of Robotics Research, 37(13-14):1632–1672.
  11. Maximum entropy density estimation with generalized regularization and an application to species distribution modeling. Journal of Machine Learning Research.
  12. Langevin dqn. arXiv preprint arXiv:2002.07282.
  13. Meta-reinforcement learning of structured exploration strategies. Advances in neural information processing systems, 31.
  14. Contextual markov decision processes. arXiv preprint arXiv:1502.02259.
  15. Leveraging demonstrations to improve online learning: Quality matters. arXiv preprint arXiv:2302.03319.
  16. Bridging imitation and online reinforcement learning: An optimistic tale. arXiv preprint arXiv:2303.11369.
  17. Deep q-learning from demonstrations. In Proceedings of the AAAI conference on artificial intelligence.
  18. Provable and practical: Efficient exploration in reinforcement learning via langevin monte carlo. In The Twelfth International Conference on Learning Representations.
  19. Zero-shot meta-learning for small-scale data from human subjects. In 2023 IEEE 11th International Conference on Healthcare Informatics (ICHI), pages 311–320. IEEE.
  20. Minimax-Optimal Policy Learning Under Unobserved Confounding. Management Science, 67(5):2870–2890.
  21. Accelerating exploration with unlabeled prior data. Advances in Neural Information Processing Systems, 36.
  22. Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971.
  23. Concentration inequalities for the empirical distribution of discrete distributions: beyond the method of types. Information and Inference: A Journal of the IMA, 9(4):813–850.
  24. Guided meta-policy search. Advances in Neural Information Processing Systems, 32.
  25. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602.
  26. Learning to adapt in dynamic, real-world environments through meta-reinforcement learning. arXiv preprint arXiv:1803.11347.
  27. Awac: Accelerating online reinforcement learning with offline datasets. arXiv preprint arXiv:2006.09359.
  28. Overcoming exploration in reinforcement learning with demonstrations. In 2018 IEEE international conference on robotics and automation (ICRA), pages 6292–6299. IEEE.
  29. Randomized prior functions for deep reinforcement learning. Advances in Neural Information Processing Systems, 31.
  30. Deep exploration via bootstrapped dqn. Advances in neural information processing systems, 29.
  31. Behaviour suite for reinforcement learning. arXiv preprint arXiv:1908.03568.
  32. (more) efficient reinforcement learning via posterior sampling. Advances in Neural Information Processing Systems, 26.
  33. Deep exploration via randomized value functions. J. Mach. Learn. Res., 20(124):1–62.
  34. Approximate thompson sampling via epistemic neural networks. In Uncertainty in Artificial Intelligence, pages 1586–1595. PMLR.
  35. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35:27730–27744.
  36. Learning complex dexterous manipulation with deep reinforcement learning and demonstrations. arXiv preprint arXiv:1709.10087.
  37. Efficient off-policy meta-reinforcement learning via probabilistic context variables. In International conference on machine learning, pages 5331–5340. PMLR.
  38. Rockafellar, R. T. (1997). Convex analysis, volume 11. Princeton university press.
  39. An information-theoretic analysis of thompson sampling. The Journal of Machine Learning Research, 17(1):2442–2471.
  40. A tutorial on thompson sampling. Foundations and Trends® in Machine Learning, 11(1):1–96.
  41. TGRL: An algorithm for teacher guided reinforcement learning. In Krause, A., Brunskill, E., Cho, K., Engelhardt, B., Sabato, S., and Scarlett, J., editors, Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pages 31077–31093. PMLR.
  42. Mastering the game of go with deep neural networks and tree search. nature, 529(7587):484–489.
  43. Mastering chess and shogi by self-play with a general reinforcement learning algorithm. arXiv preprint arXiv:1712.01815.
  44. Deterministic policy gradient algorithms. In International conference on machine learning, pages 387–395. Pmlr.
  45. Hybrid rl: Using both offline and online data can make rl efficient. arXiv preprint arXiv:2210.06718.
  46. Sequence model imitation learning with unobserved contexts. Advances in Neural Information Processing Systems, 35:17665–17676.
  47. Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards. arXiv preprint arXiv:1707.08817.
  48. Meta-learning for generalized zero-shot learning. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 6062–6069.
  49. Leveraging offline data in online reinforcement learning. In International Conference on Machine Learning, pages 35300–35338. PMLR.
  50. Impossibly good experts and how to follow them. In The Eleventh International Conference on Learning Representations.
  51. Bayesian learning via stochastic gradient langevin dynamics. In Proceedings of the 28th international conference on machine learning (ICML-11), pages 681–688.
  52. Meta-inverse reinforcement learning with probabilistic context variables. Advances in neural information processing systems, 32.
  53. Causal imitation learning with unobserved confounders. Advances in neural information processing systems, 33:12263–12274.
  54. Watch, try, learn: Meta-learning from demonstrations and reward. arXiv preprint arXiv:1906.03352.
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Authors (5)
  1. Vahid Balazadeh (8 papers)
  2. Keertana Chidambaram (3 papers)
  3. Viet Nguyen (13 papers)
  4. Rahul G. Krishnan (45 papers)
  5. Vasilis Syrgkanis (106 papers)

Summary

  • The paper proposes leveraging expert demonstrations as informative priors to improve sequential decision-making under unobserved heterogeneity.
  • Integrating expert priors significantly boosts learning efficiency, accelerating convergence and enhancing policy performance in Multi-Armed Bandit and Reinforcement Learning scenarios.
  • This approach offers both theoretical advancements in using expert knowledge and practical potential for improving AI systems in real-world applications with latent variables.

Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity

The paper explores the complexities of sequential decision-making in the presence of unobserved heterogeneity, emphasizing the value of expert demonstrations. This paper is critical in domains where decision-making entities need to learn efficiently, even when certain variability in conditions or contexts is not directly observable. The research addresses the challenges by integrating expert knowledge with advanced algorithmic approaches for learning effective policies within the context of multi-armed bandits (MABs) and reinforcement learning (RL).

Problem Setup and Methodology

The core problem addressed in this research is how to leverage expert demonstrations as prior knowledge to improve learning in the context of sequential decision-making. The paper focuses on settings where traditional learning algorithms can underperform due to unobserved heterogeneity. This issue is prevalent in real-world applications where the environment's latent variables significantly influence observable outcomes, and thus the optimal decision strategy.

To tackle this problem, the paper proposes a framework whereby expert demonstrations are not mere data points but are encoded into informative priors for the learning algorithm. The computational model uses these priors to guide the policy search in MAB and RL scenarios. A significant methodological contribution of the paper is the integration of these priors in a way that adapts dynamically to the observed data, allowing for more robust and efficient learning.

Key Results and Implications

The numerical experiments conducted in the research demonstrate the efficacy of the proposed approach. Notably, the framework exhibits improved learning efficiency over traditional methods that do not incorporate expert priors. The performance gains are quantified by accelerated convergence rates and enhanced policy performance, particularly in environments with significant unobserved heterogeneity.

Multi-Armed Bandits (MABs)

In the context of MABs, the integration of expert-demonstrations as priors leads to a marked improvement in decision efficiency. The incorporation of expert knowledge mitigates exploration costs and enhances the quality of the resulting policy.

Reinforcement Learning (RL)

In RL settings, the paper reveals substantial improvements in policy learning efficiency and effectiveness. The expert-informed priors significantly reduce the sample complexity, which is critically important in practical RL applications where sampling is costly.

Theoretical and Practical Implications

From a theoretical standpoint, this paper advances our understanding of how to effectively utilize expert knowledge in machine learning algorithms. The concept of using expert demonstrations as priors can be generalized beyond the specific cases of MABs and RL, potentially influencing a broad spectrum of machine learning tasks where unobserved heterogeneity is a concern.

Practically, the research provides a viable pathway to enhancing decision-making systems in various applications ranging from robotics to autonomous systems and beyond. As real-world environments often involve unobserved factors that influence decision outcomes, the approach proposed can significantly enhance the reliability and efficiency of AI systems deployed in such environments.

Future Directions

Future research could explore the robustness of expert-informed priors across different types of environments and decision-making problems. Additionally, investigating the scalability of this approach in increasingly complex and high-dimensional spaces would be a valuable extension. There is also potential for exploring its applicability in domains where real-time decision-making and adaptability are crucial.

Ultimately, the integration of sophisticated models for encoding expert demonstrations as priors represents not just an enhancement in algorithmic capabilities but also a bridge to further interdisciplinary approaches in AI and machine learning.

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