  We study computationally efficient methods for finding equilibria in n-player
general-sum games, specifically ones that afford complex visuomotor skills. We
show how existing methods would struggle in this setting, either
computationally or in theory. We then introduce NeuPL-JPSRO, a neural
population learning algorithm that benefits from transfer learning of skills
and converges to a Coarse Correlated Equilibrium (CCE) of the game. We show
empirical convergence in a suite of OpenSpiel games, validated rigorously by
exact game solvers. We then deploy NeuPL-JPSRO to complex domains, where our
approach enables adaptive coordination in a MuJoCo control domain and skill
transfer in capture-the-flag. Our work shows that equilibrium convergent
population learning can be implemented at scale and in generality, paving the
way towards solving real-world games between heterogeneous players with mixed
motives.
