Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling
Keywords: reinforcement learning, model-based reinforcement learning, optimistic exploration
TL;DR: We introduce a theoretically-grounded approach to optimistic exploration that leverages joint uncertainty over states and rewards for improved sample efficiency.
Abstract: Learning complex robot behavior through interactions with the environment necessitates principled exploration. Effective strategies should prioritize exploring regions of the state-action space that maximize rewards, with optimistic exploration emerging as a promising direction aligned with this idea and enabling sample-efficient reinforcement learning. However, existing methods overlook a crucial aspect: the need for optimism to be informed by a belief connecting the reward and state. To address this, we propose a practical, theoretically grounded approach to optimistic exploration based on Thompson sampling. Our approach is the first that allows for reasoning about _joint_ uncertainty over transitions and rewards for optimistic exploration. We apply our method on a set of MuJoCo and VMAS continuous control tasks. Our experiments demonstrate that optimistic exploration significantly accelerates learning in environments with sparse rewards, action penalties, and difficult-to-explore regions. Furthermore, we provide insights into when optimism is beneficial and emphasize the critical role of model uncertainty in guiding exploration.
Supplementary Material: zip
Primary Area: reinforcement learning
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Submission Number: 12371
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