Off-Policy Reinforcement Learning with High Dimensional Reward

• URL: http://arxiv.org/abs/2408.07660v1
• Date: Wed, 14 Aug 2024 16:44:56 GMT
• Title: Off-Policy Reinforcement Learning with High Dimensional Reward
• Authors: Dong Neuck Lee, Michael R. Kosorok,
• Abstract summary: Distributional RL (DRL) studies the distribution of returns with the distributional Bellman operator in a Euclidean space. We prove the contraction property of the Bellman operator even when the reward space is an infinite-dimensional separable Banach space. We propose a novel DRL algorithm that tackles problems which have been previously intractable using conventional reinforcement learning approaches.
• Score: 1.7297899469367062
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conventional off-policy reinforcement learning (RL) focuses on maximizing the expected return of scalar rewards. Distributional RL (DRL), in contrast, studies the distribution of returns with the distributional Bellman operator in a Euclidean space, leading to highly flexible choices for utility. This paper establishes robust theoretical foundations for DRL. We prove the contraction property of the Bellman operator even when the reward space is an infinite-dimensional separable Banach space. Furthermore, we demonstrate that the behavior of high- or infinite-dimensional returns can be effectively approximated using a lower-dimensional Euclidean space. Leveraging these theoretical insights, we propose a novel DRL algorithm that tackles problems which have been previously intractable using conventional reinforcement learning approaches.

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