Sample Efficient Stochastic Policy Extragradient Algorithm for Zero-Sum Markov Game

Two-player zero-sum Markov game is a fundamental problem in reinforcement learning and game theory. Although many algorithms have been proposed for solving zero-sum Markov games in the existing literature, many of them either require a full knowledge of the environment or are not sample-efficient. In this paper, we develop a fully decentralized and sample-efficient stochastic policy extragradient algorithm for solving tabular zero-sum Markov games. In particular, our algorithm utilizes multiple stochastic estimators to accurately estimate the value functions involved in the stochastic updates, and leverages entropy regularization to accelerate the convergence. Specifically, with a proper entropy-regularization parameter, we prove that the stochastic policy extragradient algorithm has a sample complexity of the order $\widetilde{\mathcal{O}}(\frac{A_{\max}}{\mu_{\text{min}}\epsilon^{5.5}(1-\gamma)^{13.5}})$ for finding a solution that achieves $\epsilon$-Nash equilibrium duality gap, where $A_{\max}$ is the maximum number of actions between the players, $\mu_{\min}$ is the lower bound of state stationary distribution, and $\gamma$ is the discount factor. Such a sample complexity result substantially improves the state-of-the-art complexity result.