Model-Free Robust Average-Reward Reinforcement Learning

• URL: http://arxiv.org/abs/2305.10504v1
• Date: Wed, 17 May 2023 18:19:23 GMT
• Title: Model-Free Robust Average-Reward Reinforcement Learning
• Authors: Yue Wang, Alvaro Velasquez, George Atia, Ashley Prater-Bennette, Shaofeng Zou
• Abstract summary: We focus on the robust average-reward MDPs under the model-free iteration setting. We design two model-free algorithms, robust relative value (RVI) TD and robust RVI Q-learning, and theoretically prove their convergence to the optimal solution.
• Score: 25.125481838479256
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robust Markov decision processes (MDPs) address the challenge of model uncertainty by optimizing the worst-case performance over an uncertainty set of MDPs. In this paper, we focus on the robust average-reward MDPs under the model-free setting. We first theoretically characterize the structure of solutions to the robust average-reward Bellman equation, which is essential for our later convergence analysis. We then design two model-free algorithms, robust relative value iteration (RVI) TD and robust RVI Q-learning, and theoretically prove their convergence to the optimal solution. We provide several widely used uncertainty sets as examples, including those defined by the contamination model, total variation, Chi-squared divergence, Kullback-Leibler (KL) divergence and Wasserstein distance.

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