Theoretical Foundations of Reinforcement Learning

Theoretical Foundations of Reinforcement Learning

Dive into the theoretical underpinnings of reinforcement learning in this comprehensive 2-hour and 44-minute IEEE lecture. Explore key concepts, algorithms, and mathematical frameworks that form the backbone of this powerful machine learning technique. Gain a deep understanding of the fundamental principles driving reinforcement learning, including Markov decision processes, value functions, and policy optimization. Analyze the theoretical guarantees and limitations of various reinforcement learning approaches, and discover how these foundations inform practical applications in robotics, game theory, and decision-making systems.