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Workshop: Workshop on Reinforcement Learning Theory

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

Yue Guan · Qifan Zhang · Panagiotis Tsiotras


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 entropy regularization, the algorithm converges to a Nash equilibrium. Numerical simulations verify that the proposed algorithm converges to Nash with a major speed-up over existing algorithms.

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