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Personalized Reinforcement Learning with a Budget of Policies (2401.06514v1)

Published 12 Jan 2024 in cs.LG

Abstract: Personalization in ML tailors models' decisions to the individual characteristics of users. While this approach has seen success in areas like recommender systems, its expansion into high-stakes fields such as healthcare and autonomous driving is hindered by the extensive regulatory approval processes involved. To address this challenge, we propose a novel framework termed represented Markov Decision Processes (r-MDPs) that is designed to balance the need for personalization with the regulatory constraints. In an r-MDP, we cater to a diverse user population, each with unique preferences, through interaction with a small set of representative policies. Our objective is twofold: efficiently match each user to an appropriate representative policy and simultaneously optimize these policies to maximize overall social welfare. We develop two deep reinforcement learning algorithms that efficiently solve r-MDPs. These algorithms draw inspiration from the principles of classic K-means clustering and are underpinned by robust theoretical foundations. Our empirical investigations, conducted across a variety of simulated environments, showcase the algorithms' ability to facilitate meaningful personalization even under constrained policy budgets. Furthermore, they demonstrate scalability, efficiently adapting to larger policy budgets.

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References (43)
  1. MO-Gym: A Library of Multi-Objective Reinforcement Learning Environments. In Proceedings of the 34th Benelux Conference on Artificial Intelligence BNAIC/Benelearn 2022.
  2. OR Forum—A POMDP approach to personalize mammography screening decisions. Operations Research, 60(5): 1019–1034.
  3. Learning all optimal policies with multiple criteria. In Proceedings of the 25th international conference on Machine learning, 41–47.
  4. Adapting Reinforcement Learning Treatment Policies Using Limited Data to Personalize Critical Care. INFORMS Journal on Data Science, 1(1): 27–49.
  5. A randomized trial of closed-loop control in children with type 1 diabetes. New England Journal of Medicine, 383(9): 836–845.
  6. Personalized robo-advising: Enhancing investment through client interaction. Management Science, 68(4): 2485–2512.
  7. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1): 1–22.
  8. Reinforcement learning for personalization: A systematic literature review. Data Science, 3(2): 107–147.
  9. Algorithms and learning for fair portfolio design. In Proceedings of the 22nd ACM Conference on Economics and Computation, 371–389.
  10. pH-RL: A personalization architecture to bring reinforcement learning to health practice. In Machine Learning, Optimization, and Data Science: 7th International Conference, LOD 2021, Grasmere, UK, October 4–8, 2021, Revised Selected Papers, Part I, 265–280. Springer.
  11. End-to-end Personalization of Digital Health Interventions using Raw Sensor Data with Deep Reinforcement Learning. In IEEE/WIC/ACM International Conference on Web Intelligence, 258–264.
  12. Model-agnostic meta-learning for fast adaptation of deep networks. In International conference on machine learning, 1126–1135. PMLR.
  13. Cluster-based social reinforcement learning. arXiv preprint arXiv:2003.00627.
  14. Exploring clustering techniques for effective reinforcement learning based personalization for health and wellbeing. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI), 813–820. IEEE.
  15. Safe exploration for reinforcement learning. In ESANN, 143–148. Citeseer.
  16. Personalization of health interventions using cluster-based reinforcement learning. In PRIMA 2018: Principles and Practice of Multi-Agent Systems: 21st International Conference, Tokyo, Japan, October 29-November 2, 2018, Proceedings 21, 467–475. Springer.
  17. A practical guide to multi-objective reinforcement learning and planning. Autonomous Agents and Multi-Agent Systems, 36(1): 1–59.
  18. The 37 Implementation Details of Proximal Policy Optimization. In ICLR Blog Track.
  19. JDRF. 2022. FDA Authorizes a Fourth Artificial Pancreas System. https://www.jdrf.org/blog/2022/01/28/fda-authorizes-a-fourth-artificial-pancreas-system/. Accessed: 2024-01-11.
  20. Safety-constrained reinforcement learning for MDPs. In Tools and Algorithms for the Construction and Analysis of Systems: 22nd International Conference, TACAS 2016, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2016, Eindhoven, The Netherlands, April 2-8, 2016, Proceedings, 130–146. Springer.
  21. Feature-based Individual Fairness in k-clustering. In Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2772–2774.
  22. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
  23. Prea: Personalized recommendation algorithms toolkit. The Journal of Machine Learning Research, 13(1): 2699–2703.
  24. Littman, M. L. 1994. Markov games as a framework for multi-agent reinforcement learning. In Machine learning proceedings 1994, 157–163. Elsevier.
  25. Lloyd, S. 1982. Least squares quantization in PCM. IEEE transactions on information theory, 28(2): 129–137.
  26. MacQueen, J. 1967. Classification and analysis of multivariate observations. In 5th Berkeley Symp. Math. Statist. Probability, 281–297.
  27. Socially fair reinforcement learning. arXiv preprint arXiv:2208.12584.
  28. AgentX: Using reinforcement learning to improve the effectiveness of intelligent tutoring systems. In Intelligent Tutoring Systems: 7th International Conference, ITS 2004, Maceió, Alagoas, Brazil, August 30-September 3, 2004. Proceedings 7, 564–572. Springer.
  29. Safe exploration in markov decision processes. arXiv preprint arXiv:1205.4810.
  30. Federated reinforcement learning for fast personalization. In 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), 123–127. IEEE.
  31. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32.
  32. Lyapunov design for safe reinforcement learning. Journal of Machine Learning Research, 3(Dec): 803–832.
  33. Trust region policy optimization. In International conference on machine learning, 1889–1897. PMLR.
  34. High-dimensional continuous control using generalized advantage estimation. arXiv preprint arXiv:1506.02438.
  35. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.
  36. Personalized recommendation in social tagging systems using hierarchical clustering. In Proceedings of the 2008 ACM conference on Recommender systems, 259–266.
  37. Narrowing reinforcement learning: Overcoming the cold start problem for personalized health interventions. In PRIMA 2018: Principles and Practice of Multi-Agent Systems: 21st International Conference, Tokyo, Japan, October 29-November 2, 2018, Proceedings 21, 312–327. Springer.
  38. Deepmind control suite. arXiv preprint arXiv:1801.00690.
  39. MuJoCo: A physics engine for model-based control. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 5026–5033. IEEE.
  40. dm__\__control: Software and tasks for continuous control. Software Impacts, 6: 100022.
  41. Personalized news recommendation: Methods and Challenges. ACM Transactions on Information Systems, 41(1): 1–50.
  42. RLPer: A reinforcement learning model for personalized search. In Proceedings of The Web Conference 2020, 2298–2308.
  43. Group-driven reinforcement learning for personalized mhealth intervention. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part I, 590–598. Springer.
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Summary

  • The paper introduces an r-MDP framework that simplifies personalization by using a limited set of representative policies to effectively match diverse user preferences.
  • It leverages two deep reinforcement learning algorithms—one resembling Expectation-Maximization and an end-to-end differentiable approach—to iteratively optimize policy-user assignments.
  • Empirical results show that these methods outperform conventional baselines, offering scalable personalization under strict regulatory constraints.

Introduction

Machine learning personalization enhances user-centric experiences across numerous applications, but its integration into high-stakes scenarios like healthcare and autonomous driving is complicated by demanding regulatory reviews. The complexities stem from ensuring that newly developed personalized ML models are safe and effective for each user. Traditional approaches requiring individual assessments for each user-specific model pose significant regulatory burdens. To navigate these constraints, a new framework, represented Markov Decision Processes (r-MDPs), has been proposed, offering a novel perspective on achieving personalization within the confines of practical policy limits.

Framework and Objectives

An r-MDP focuses on catering to a diverse user population through a limited, well-defined set of policies, each representing the preferences of different user groups. The goal is twofold: match users to the most suitable policy and refine these policies to maximize collective satisfaction, or social welfare. The proposed framework simplifies the complex challenge of numerous personal policies by leveraging a more manageable number of representative policies, which are easier to regulate and deploy.

Central to this approach is the division of the overall task into a two-fold problem: one part concentrating on policy optimization for given user-to-representative pairings, and the other refining these pairings given the fixed policies. The researchers put forth two deep learning algorithms drawing parallel to classic clustering techniques, with theoretical guarantees of progression towards local optima.

Methodology

The methodologies revolve around two deep reinforcement learning algorithms: one analogous to Expectation-Maximization (EM) commonly seen in clustering, and another utilizing end-to-end training with differentiable objectives. The former iteratively assigns users to policies seeking to maximize satisfaction, with the assignment serving as the basis for subsequent policy improvements. The latter algorithm blurs the line between assigning users and improving policies by updating assignment probabilities within the policy optimization process.

These algorithms are substantiated through empirical studies in simulated environments. The Resource Gathering environment serves as a manageable testbed where each user seeks to collect location-specific resources efficiently. The performance in more complex scenarios is tested using the MuJoCo simulator, which involves controlling robots with high-dimensional and continuous actions, closely resembling the type of applications the framework aims to serve.

Empirical Findings

Empirical evaluations reveal that our algorithms significantly outperform conventional baselines, which lack the nuanced handling of policy budget constraints intrinsic to r-MDPs. Not only do our methods adapt to varying policy limitations effectively, but even with a constrained number of policies, they demonstrate a capacity for substantial personalization. This translates into practical implications where regulatory assessments are stringent, and policies need to be limited yet effective for various user groups.

Looking Forward

While this paper lays the groundwork for personalizing ML solutions under regulatory constraints, it also points toward future research directions. Among these is the incorporation of fairness considerations into social welfare optimization and the examination of real-world applications beyond simulations. The paper's findings advocate for an innovative blend of ML personalization with regulatory viability, promising a path forward for personalized ML solutions in critical sectors.

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