Personalized Reinforcement Learning with a Budget of Policies

• URL: http://arxiv.org/abs/2401.06514v1
• Date: Fri, 12 Jan 2024 11:27:55 GMT
• Title: Personalized Reinforcement Learning with a Budget of Policies
• Authors: Dmitry Ivanov, Omer Ben-Porat
• Abstract summary: Personalization in machine learning (ML) tailors models' decisions to the individual characteristics of users. 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. We develop two deep reinforcement learning algorithms that efficiently solve r-MDPs.
• Score: 9.846353643883443
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Personalization in machine learning (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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