In this talk, we'll dive into the exciting world of reinforcement learning and explore how policy gradient methods can help agents learn winning strategies in complex and dynamic environments. We'll take a closer look at the convergence properties of these methods in stochastic games, where the rules of the game may change over time, and show that they can converge to second-order stationary Nash equilibrium policies with high probability. We'll also discuss how the convergence rate can be improved for deterministic Nash policies, enabling agents to learn optimal strategies in a finite number of iterations. Join us as we uncover the secrets of reinforcement learning and its potential for designing efficient algorithms that can learn winning strategies in a variety of settings.