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arXiv · 2102.08903

Provably Efficient Policy Optimization for Two-Player Zero-Sum Markov Games

Yulai Zhao·Yuandong Tian·Jason D. Lee·Simon S. Du
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Abstract

Policy-based methods with function approximation are widely used for solving two-player zero-sum games with large state and/or action spaces. However, it remains elusive how to obtain optimization and statistical guarantees for such algorithms. We present a new policy optimization algorithm with function approximation and prove that under standard regularity conditions on the Markov game and the function approximation class, our algorithm finds a near-optimal policy within a polynomial number of samples and iterations. To our knowledge, this is the first provably efficient policy optimization algorithm with function approximation that solves two-player zero-sum Markov games.

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BibTeXRIS

Yulai Zhao, Yuandong Tian, Jason D. Lee, Simon S. Du. 2021-02-17. Provably Efficient Policy Optimization for Two-Player Zero-Sum Markov Games. https://arxiv.org/abs/2102.08903

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