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Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning by Shi, Laixi; Gai, Jingchu; Mazumdar, Eric; Chi, Yuejie; Wierman, Adam is a scholarly article available to read on EtoBox.

What is Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning about?

Standard multi-agent reinforcement learning (MARL) algorithms are vulnerable to sim-to-real gaps. To address this, distributionally robust Markov games (RMGs) have been proposed to enhance robustness in MARL by optimizing the worst-case performance when game dynamics shift within a prescribed uncertainty set. RMGs remains under-explored, from reasonable problem formulation to the development of sample-efficient algorithms. Two notorious and open challenges are the formulation of the uncertainty set and whether the corresponding RMGs can overcome the curse of multiagency, where the sample complexity scales exponentially with the number of agents. In this work, we propose a natural class of RMGs inspired by behavioral economics, where each agent's uncertainty set is shaped by both the environment and the integrated behavior of other agents. We first establish the well-posedness of this class of RMGs by proving the existence of game-theoretic solutions such as robust Nash equilibria and coarse correlated equilibria (CCE). Assuming access to a generative model, we then introduce a sample-efficient algorithm for learning the CCE whose sample complexity scales polynomially with all relev

Author

Shi, Laixi; Gai, Jingchu; Mazumdar, Eric; Chi, Yuejie; Wierman, Adam

Published

2024

Language

EN