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A Bayesian Learning Algorithm for Unknown Zero-sum Stochastic Games with an Arbitrary Opponent
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, PMLR 238:3880-3888, 2024.
Abstract
In this paper, we propose Posterior Sampling Reinforcement Learning for Zero-sum Stochastic Games (PSRL-ZSG), the first online learning algorithm that achieves Bayesian regret bound of \tilde\mathcal{O}(HS\sqrt{AT}) in the infinite-horizon zero-sum stochastic games with average-reward criterion. Here H is an upper bound on the span of the bias function, S is the number of states, A is the number of joint actions and T is the horizon. We consider the online setting where the opponent can not be controlled and can take any arbitrary time-adaptive history-dependent strategy. Our regret bound improves on the best existing regret bound of \tilde\mathcal{O}(\sqrt[3]{DS^2AT^2}) by Wei et al., (2017) under the same assumption and matches the theoretical lower bound in T.