Mean-field games have been used as a theoretical tool to obtain an approximate Nash equilibrium for symmetric and anonymous N-player games.However, limiting applicability, existing theoretical results assume variations of a “population generative model”, which allows arbitrary modifications of the population distribution by the learning algorithm.Moreover, learning algorithms typically work on abstract simulators with population instead of the N-player game.Instead, we show that N agents running…