Average-Reward Reinforcement Learning with Entropy Regularization

• URL: http://arxiv.org/abs/2501.09080v1
• Date: Wed, 15 Jan 2025 19:00:46 GMT
• Title: Average-Reward Reinforcement Learning with Entropy Regularization
• Authors: Jacob Adamczyk, Volodymyr Makarenko, Stas Tiomkin, Rahul V. Kulkarni,
• Abstract summary: We develop algorithms for solving entropy-regularized average-reward RL problems with function. We experimentally validate our method, comparing it with existing algorithms on standard benchmarks for RL.
• Score: 4.8748194765816955
• License:
• Abstract: The average-reward formulation of reinforcement learning (RL) has drawn increased interest in recent years due to its ability to solve temporally-extended problems without discounting. Independently, RL algorithms have benefited from entropy-regularization: an approach used to make the optimal policy stochastic, thereby more robust to noise. Despite the distinct benefits of the two approaches, the combination of entropy regularization with an average-reward objective is not well-studied in the literature and there has been limited development of algorithms for this setting. To address this gap in the field, we develop algorithms for solving entropy-regularized average-reward RL problems with function approximation. We experimentally validate our method, comparing it with existing algorithms on standard benchmarks for RL.

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