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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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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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