$O(T^{-1})$ Convergence of Optimistic-Follow-the-Regularized-Leader in Two-Player Zero-Sum Markov Games Download PDF

Published: 01 Feb 2023, 19:22, Last Modified: 13 Feb 2023, 23:27ICLR 2023 posterReaders: Everyone
Keywords: multi-agent reinforcement learning, policy optimization, Markov game
Abstract: We prove that the optimistic-follow-the-regularized-leader (OFTRL) algorithm, together with smooth value updates, finds an $O(T^{−1})$ approximate Nash equilibrium in $T$ iterations for two-player zero-sum Markov games with full information. This improves the $\tilde{O}(T^{−5/6})$ convergence rate recently shown by Zhang et al (2022). The refined analysis hinges on two essential ingredients. First, the sum of the regrets of the two players, though not necessarily non-negative as in normal-form games, is approximately non-negative in Markov games. This property allows us to bound the second-order path lengths of the learning dynamics. Second, we prove a tighter algebraic inequality regarding the weights deployed by OFTRL that shaves an extra $\log T$ factor. This crucial improvement enables the inductive analysis that leads to the final $O(T^{−1})$ rate.
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