On the convergence of policy gradient methods to Nash equilibria in general stochastic games
This addresses the challenge of learning in complex stochastic games for researchers and practitioners in multi-agent reinforcement learning, representing a significant theoretical advance beyond specific game classes.
The paper tackles the problem of understanding the convergence of policy gradient methods to Nash equilibria in general stochastic games, showing that second-order stationary policies are locally attracting with high probability and achieving an O(1/√n) convergence rate, with finite-time convergence for deterministic Nash policies.
Learning in stochastic games is a notoriously difficult problem because, in addition to each other's strategic decisions, the players must also contend with the fact that the game itself evolves over time, possibly in a very complicated manner. Because of this, the convergence properties of popular learning algorithms - like policy gradient and its variants - are poorly understood, except in specific classes of games (such as potential or two-player, zero-sum games). In view of this, we examine the long-run behavior of policy gradient methods with respect to Nash equilibrium policies that are second-order stationary (SOS) in a sense similar to the type of sufficiency conditions used in optimization. Our first result is that SOS policies are locally attracting with high probability, and we show that policy gradient trajectories with gradient estimates provided by the REINFORCE algorithm achieve an $\mathcal{O}(1/\sqrt{n})$ distance-squared convergence rate if the method's step-size is chosen appropriately. Subsequently, specializing to the class of deterministic Nash policies, we show that this rate can be improved dramatically and, in fact, policy gradient methods converge within a finite number of iterations in that case.