Last-Iterate Convergence with Full- and Noisy-Information Feedback in Two-Player Zero-Sum Games. (arXiv:2208.09855v1 [cs.GT])

The theory of learning in games is prominent in the AI community, motivated
by several rising applications such as multi-agent reinforcement learning and
Generative Adversarial Networks. We propose Mutation-driven Multiplicative
Weights Update (M2WU) for learning an equilibrium in two-player zero-sum
normal-form games and prove that it exhibits the last-iterate convergence
property in both full- and noisy-information feedback settings. In the
full-information feedback setting, the players observe their exact gradient
vectors of the utility functions. On the other hand, in the …

arxiv convergence feedback games information iterate