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cs.GT2022
Last-Iterate Convergence with Full and Noisy Feedback in Two-Player Zero-Sum Games
Kenshi Abe, Kaito Ariu, Mitsuki Sakamoto +2
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-i…
cs.GT2022★ 5 cited
Mutation-Driven Follow the Regularized Leader for Last-Iterate Convergence in Zero-Sum Games
Kenshi Abe, Mitsuki Sakamoto, Atsushi Iwasaki
In this study, we consider a variant of the Follow the Regularized Leader (FTRL) dynamics in two-player zero-sum games. FTRL is guaranteed to converge to a Nash equilibrium when ti…