  Solution concepts such as Nash Equilibria, Correlated Equilibria, and Coarse
Correlated Equilibria are useful components for many multiagent machine
learning algorithms. Unfortunately, solving a normal-form game could take
prohibitive or non-deterministic time to converge, and could fail. We introduce
the Neural Equilibrium Solver which utilizes a special equivariant neural
network architecture to approximately solve the space of all games of fixed
shape, buying speed and determinism. We define a flexible equilibrium selection
framework, that is capable of uniquely selecting an equilibrium that minimizes
relative entropy, or maximizes welfare. The network is trained without needing
to generate any supervised training data. We show remarkable zero-shot
generalization to larger games. We argue that such a network is a powerful
component for many possible multiagent algorithms.
