  Save for some special cases, current training methods for Generative
Adversarial Networks (GANs) are at best guaranteed to converge to a `local Nash
equilibrium` (LNE). Such LNEs, however, can be arbitrarily far from an actual
Nash equilibrium (NE), which implies that there are no guarantees on the
quality of the found generator or classifier. This paper proposes to model GANs
explicitly as finite games in mixed strategies, thereby ensuring that every LNE
is an NE. With this formulation, we propose a solution method that is proven to
monotonically converge to a resource-bounded Nash equilibrium (RB-NE): by
increasing computational resources we can find better solutions. We empirically
demonstrate that our method is less prone to typical GAN problems such as mode
collapse, and produces solutions that are less exploitable than those produced
by GANs and MGANs, and closely resemble theoretical predictions about NEs.
