NashPG is a policy-gradient method with iteratively refined regularization that guarantees monotonic convergence to Nash equilibria in two-player zero-sum extensive-form games and scales to large benchmarks.
A unified game-theoretic approach to multiagent reinforcement learning
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NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria
NashPG is a policy-gradient method with iteratively refined regularization that guarantees monotonic convergence to Nash equilibria in two-player zero-sum extensive-form games and scales to large benchmarks.