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arXiv · 2009.00162

Learning Nash Equilibria in Zero-Sum Stochastic Games via Entropy-Regularized Policy Approximation

Yue Guan·Qifan Zhang·Panagiotis Tsiotras
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Abstract

We explore the use of policy approximations to reduce the computational cost of learning Nash equilibria in zero-sum stochastic games. We propose a new Q-learning type algorithm that uses a sequence of entropy-regularized soft policies to approximate the Nash policy during the Q-function updates. We prove that under certain conditions, by updating the regularized Q-function, the algorithm converges to a Nash equilibrium. We also demonstrate the proposed algorithm's ability to transfer previous training experiences, enabling the agents to adapt quickly to new environments. We provide a dynamic hyper-parameter scheduling scheme to further expedite convergence. Empirical results applied to a number of stochastic games verify that the proposed algorithm converges to the Nash equilibrium, while exhibiting a major speed-up over existing algorithms.

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Yue Guan, Qifan Zhang, Panagiotis Tsiotras. 2020-09-01. Learning Nash Equilibria in Zero-Sum Stochastic Games via Entropy-Regularized Policy Approximation. https://arxiv.org/abs/2009.00162

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