Last-Iterate Convergence with Full and Noisy Feedback in Two-Player Zero-Sum Games

1 code implementation • 21 Aug 2022 • Kenshi Abe, Kaito Ariu, Mitsuki Sakamoto, Kentaro Toyoshima, Atsushi Iwasaki

This paper proposes Mutation-Driven Multiplicative Weights Update (M2WU) for learning an equilibrium in two-player zero-sum normal-form games and proves that it exhibits the last-iterate convergence property in both full and noisy feedback settings.

Multi-agent Reinforcement Learning