Learning Simulation-Based Games from Data

We tackle a fundamental problem in empirical game-theoretic analysis (EGTA), that of learning equilibria of simulation-based games. Such games cannot be described in analytical form; instead, a blackbox simulator can be queried to obtain noisy samples of utilities. Our approach to EGTA is in the spirit of probably approximately correct learning. We design algorithms that learn empirical games, which uniformly approximate the utilities of simulation-based games from finitely many samples. Our methodology learns all the equilibria of simulation-based games, as opposed to a single one.