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arXiv · 2401.15240

Near-Optimal Policy Optimization for Correlated Equilibrium in General-Sum Markov Games

Yang Cai·Haipeng Luo·Chen-Yu Wei·Weiqiang Zheng
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Abstract

We study policy optimization algorithms for computing correlated equilibria in multi-player general-sum Markov Games. Previous results achieve $O(T^{-1/2})$ convergence rate to a correlated equilibrium and an accelerated $O(T^{-3/4})$ convergence rate to the weaker notion of coarse correlated equilibrium. In this paper, we improve both results significantly by providing an uncoupled policy optimization algorithm that attains a near-optimal $\tilde{O}(T^{-1})$ convergence rate for computing a correlated equilibrium. Our algorithm is constructed by combining two main elements (i) smooth value updates and (ii) the optimistic-follow-the-regularized-leader algorithm with the log barrier regularizer.

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Yang Cai, Haipeng Luo, Chen-Yu Wei, Weiqiang Zheng. 2024-01-26. Near-Optimal Policy Optimization for Correlated Equilibrium in General-Sum Markov Games. https://arxiv.org/abs/2401.15240

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