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0111 · May 202219922001200920172026
2 results for IIEFGs

This paper tackles sample-efficient reinforcement learning for partially observable Markov games.

problem Learning in partially observable Markov games with incomplete information.
method A simple algorithm combining optimism and Maximum Likelihood Estimation (MLE) for self-play, and a variant of optimistic MLE for adversarial opponents.
result The proposed algorithms achieve approximate Nash, correlated, and coarse correlated equilibria in polynomial samples for weakly revealing POMGs.