Lecture
This lecture provides an introduction to reinforcement learning (RL), focusing on its fundamental concepts and applications. The instructor begins by defining intelligence as the ability to learn across various tasks, emphasizing the need for general-purpose learning systems. The lecture illustrates the trial-and-error learning process inherent in RL, using examples such as a humanoid robot learning to navigate a parkour course through repeated attempts. Key aspects of RL are discussed, including the importance of reward functions in specifying goals and the challenges of long-term dependencies. The instructor highlights notable successes in RL, such as AlphaGo and AlphaZero, and outlines the structure of Markov Decision Processes (MDPs) as a framework for RL problems. The lecture also covers the distinction between model-based and model-free learning, exploration strategies, and various algorithms like SARSA and Q-learning. Finally, the instructor touches on deep reinforcement learning and the use of neural networks to approximate value functions, concluding with a discussion on policy optimization methods.