标题： 带有熵正则化的平均奖励强化学习 显示英文标题

标题： Average-Reward Reinforcement Learning with Entropy Regularization

摘要： 平均奖励公式化强化学习（RL）近年来引起了越来越多的关注，因为它能够在不进行折扣的情况下解决时间扩展问题。 独立地，RL算法受益于熵正则化：一种用于使最优策略随机化的方法，从而对噪声更具鲁棒性。 尽管这两种方法各有优势，但文献中对熵正则化与平均奖励目标相结合的研究并不充分，且对此设置的算法开发有限。 为弥补这一领域空白，我们开发了用于解决具有函数近似的熵正则化平均奖励RL问题的算法。 我们通过实验验证了我们的方法，并在标准RL基准上将其与现有算法进行了比较。 显示英文摘要

摘要： 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.