Extragradient with player sampling for faster Nash equilibrium finding
Abstract
Data-driven modeling increasingly requires to find a Nash equilibrium in multi-player games, e.g. when training GANs. In this paper, we analyse a new extra-gradient method for Nash equilibrium finding, that performs gradient extrapolations and updates on a random subset of players at each iteration. This approach provably exhibits a better rate of convergence than full extra-gradient for non-smooth convex games with noisy gradient oracle. We propose an additional variance reduction mechanism to obtain speed-ups in smooth convex games. Our approach makes extrapolation amenable to massive multiplayer settings, and brings empirical speed-ups, in particular when using a heuristic cyclic sampling scheme. Most importantly, it allows to train faster and better GANs and mixtures of GANs.
Cite
@article{arxiv.1905.12363,
title = {Extragradient with player sampling for faster Nash equilibrium finding},
author = {Carles Domingo Enrich and Samy Jelassi and Carles Domingo-Enrich and Damien Scieur and Arthur Mensch and Joan Bruna},
journal= {arXiv preprint arXiv:1905.12363},
year = {2020}
}