You can now learn approximate Nash equilibria in multi-agent games with unknown dynamics in polynomial sample complexity, and the algorithm provably detects when equilibria don't exist—solving a fundamental challenge in multi-agent reinforcement learning.
This paper develops the first PAC learning algorithm for concurrent stochastic games where multiple agents learn Nash equilibria despite uncertain transition dynamics. The algorithm maintains confidence sets over game transitions and solves robust games to find welfare-optimal approximate equilibria, with a novel mechanism to certify when exact equilibria don't exist.