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• W3084233102 abstract "We consider the problem of two-player zero-sum games. This problem is formulated as a min-max Markov game in the literature. The solution of this game, which is the min-max payoff, starting from a given state is called the min-max value of the state. In this work, we compute the solution of the two-player zero-sum game utilizing the technique of successive relaxation that has been successfully applied in the literature to compute a faster value iteration algorithm in the context of Markov Decision Processes. We extend the concept of successive relaxation to the setting of two-player zero-sum games. We show that, under a special structure on the game, this technique facilitates faster computation of the min-max value of the states. We then derive a generalized minimax Q-learning algorithm that computes the optimal policy when the model information is not known. Finally, we prove the convergence of the proposed generalized minimax Q-learning algorithm utilizing stochastic approximation techniques, under an assumption on the boundedness of iterates. Through experiments, we demonstrate the effectiveness of our proposed algorithm." @default.
• W3084233102 created "2020-09-14" @default.
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• W3084233102 date "2022-09-01" @default.
• W3084233102 modified "2023-09-30" @default.
• W3084233102 title "A Generalized Minimax Q-Learning Algorithm for Two-Player Zero-Sum Stochastic Games" @default.
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• W3084233102 doi "https://doi.org/10.1109/tac.2022.3159453" @default.
• W3084233102 hasPublicationYear "2022" @default.
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