  We introduce the use of generative adversarial learning to compute equilibria
in general game-theoretic settings, specifically the generalized Nash
equilibrium (GNE) in pseudo-games, and its specific instantiation as the
competitive equilibrium (CE) in Arrow-Debreu competitive economies.
Pseudo-games are a generalization of games in which players' actions affect not
only the payoffs of other players but also their feasible action spaces.
Although the computation of GNE and CE is intractable in the worst-case, i.e.,
PPAD-hard, in practice, many applications only require solutions with high
accuracy in expectation over a distribution of problem instances. We introduce
Generative Adversarial Equilibrium Solvers (GAES): a family of generative
adversarial neural networks that can learn GNE and CE from only a sample of
problem instances. We provide computational and sample complexity bounds, and
apply the framework to finding Nash equilibria in normal-form games, CE in
Arrow-Debreu competitive economies, and GNE in an environmental economic model
of the Kyoto mechanism.
