Processing math: 100%
Skip to yearly menu bar Skip to main content


Oral

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

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

Abstract: We study policy optimization algorithms for computing correlated equilibria in multi-player general-sum Markov Games. Previous results achieve ˜O(T1/2) convergence rate to a correlated equilibrium and an accelerated ˜O(T3/4) 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 ˜O(T1) convergence rate for computing a correlated equilibrium. Our algorithm is constructed by combining two main elements (i) smooth value updates and (ii) the \emph{optimistic-follow-the-regularized-leader} algorithm with the log barrier regularizer.

Chat is not available.