Explore a 46-minute lecture on independent learning dynamics for stochastic games in multi-agent reinforcement learning. Delve into the challenges of applying classical reinforcement learning to multi-agent scenarios and discover recently proposed independent learning dynamics that guarantee convergence in stochastic games. Examine both zero-sum and single-controller identical-interest settings, while revisiting key concepts from game theory and reinforcement learning. Learn about the mathematical novelties in analyzing these dynamics, including differential inclusion approximation and Lyapunov functions. Gain insights into topics such as Nash equilibrium, fictitious play, and model-free individual Q-learning, all within the context of dynamic multi-agent environments.