  Two-player, constant-sum games are well studied in the literature, but there
has been limited progress outside of this setting. We propose Joint
Policy-Space Response Oracles (JPSRO), an algorithm for training agents in
n-player, general-sum extensive form games, which provably converges to an
equilibrium. We further suggest correlated equilibria (CE) as promising
meta-solvers, and propose a novel solution concept Maximum Gini Correlated
Equilibrium (MGCE), a principled and computationally efficient family of
solutions for solving the correlated equilibrium selection problem. We conduct
several experiments using CE meta-solvers for JPSRO and demonstrate convergence
on n-player, general-sum games.
