AI RESEARCH
Taming the Curses of Multiagency in Robust Markov Games with Large State Space through Linear Function Approximation
arXiv CS.LG
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ArXi:2605.03125v1 Announce Type: new Multi-agent reinforcement learning (MARL) holds great potential but faces robustness challenges due to environmental uncertainty. To address this, distributionally robust Marko games (RMGs) optimize worst-case performance when the environment deviates from the nominal model within a uncertainty set. Beyond robustness, an equally urgent goal for MARL is data efficiency -- sampling from vast state and action spaces that grow exponentially with the number of agents potentially leads to the curse of multiagency.