A Differential Perspective on Distributional Reinforcement Learning

• URL: http://arxiv.org/abs/2506.03333v1
• Date: Tue, 03 Jun 2025 19:26:25 GMT
• Title: A Differential Perspective on Distributional Reinforcement Learning
• Authors: Juan Sebastian Rojas, Chi-Guhn Lee,
• Abstract summary: We extend distributional reinforcement learning to the average-reward setting, where an agent aims to optimize the reward received per time-step.<n>In particular, we utilize a quantile-based approach to develop the first set of algorithms that can successfully learn and/or optimize the long-run per-step reward distribution.
• Score: 7.028778922533688
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To date, distributional reinforcement learning (distributional RL) methods have exclusively focused on the discounted setting, where an agent aims to optimize a potentially-discounted sum of rewards over time. In this work, we extend distributional RL to the average-reward setting, where an agent aims to optimize the reward received per time-step. In particular, we utilize a quantile-based approach to develop the first set of algorithms that can successfully learn and/or optimize the long-run per-step reward distribution, as well as the differential return distribution of an average-reward MDP. We derive proven-convergent tabular algorithms for both prediction and control, as well as a broader family of algorithms that have appealing scaling properties. Empirically, we find that these algorithms consistently yield competitive performance when compared to their non-distributional equivalents, while also capturing rich information about the long-run reward and return distributions.

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