Reinforcement Learning: Basics and Applications

This lecture introduces the fundamentals of reinforcement learning, focusing on the concept of learning to maximize rewards. It covers topics such as Markov Decision Processes, policy gradient methods, and the pros and cons of reinforcement learning. Real-world applications in robotics, autonomous driving, and power grid control are discussed. The lecture also explores recent advances in reinforcement learning, including deep Q-learning and AlphaZero achieving grandmaster level in games. Various reinforcement learning projects and algorithms are presented, highlighting the importance of policy optimization and model-based approaches.