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arXiv · 1905.12363

Extragradient with player sampling for faster Nash equilibrium finding

Carles Domingo Enrich·Samy Jelassi·Carles Domingo-Enrich·Damien Scieur·Arthur Mensch·Joan Bruna
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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.

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BibTeXRIS

Carles Domingo Enrich, Samy Jelassi, Carles Domingo-Enrich, Damien Scieur, Arthur Mensch, Joan Bruna. 2019-05-29. Extragradient with player sampling for faster Nash equilibrium finding. https://arxiv.org/abs/1905.12363

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