Bellman Optimality of Average-Reward Robust Markov Decision Processes with a Constant Gain

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• Shengbo Wang (University of Southern California)
• Thursday 13 November 2025, 14:40-15:20
• Seminar Room 1, Newton Institute.

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SCLW01 - Bridging Stochastic Control And Reinforcement Learning: Theories and Applications

Learning and optimal control under robust Markov decision processes (MDPs) have received increasing attention, yet most existing theory, algorithms, and applications focus on finite-horizon or discounted models. The average-reward formulation, while natural in many operations research and management contexts, remains underexplored. This is primarily because the dynamic programming foundations are technically challenging and only partially understood, with several fundamental questions remaining open. This paper steps toward a general framework for average-reward robust MDPs by analyzing the constant-gain setting. We study the average-reward robust control problem with possible information asymmetries between the controller and an S-rectangular adversary. Our analysis centers on the constant-gain robust Bellman equation, examining both the existence of solutions and their relationship to the optimal average reward. Specifically, we identify when solutions to the robust Bellman equation characterize the optimal average reward and stationary policies, and we provide sufficient conditions ensuring solutions’ existence. These findings expand the dynamic programming theory for average-reward robust MDPs and lay a foundation for robust dynamic decision making under long-run average criteria in operational environments.

This talk is part of the Isaac Newton Institute Seminar Series series.

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