Lecture
This lecture covers the fundamentals of policy gradient methods in reinforcement learning. It begins with an introduction to the log-likelihood trick, which is essential for evaluating average rewards. The instructor explains the transition from batch to online learning, emphasizing the importance of the log-likelihood trick in this process. Key concepts such as the basic idea of policy gradients, examples of one-step horizons, and the significance of subtracting a reward baseline are discussed. The lecture also includes a quiz to assess understanding of the material. The instructor draws parallels between policy gradient methods and perceptrons, highlighting the similarities in their update rules. The discussion progresses to multiple time steps, where the focus shifts to maximizing expected returns. The lecture concludes with a summary of the learning outcomes, emphasizing the importance of understanding the statistical weights in policy gradient algorithms and the benefits of using baselines to reduce noise in online learning. Overall, this lecture provides a comprehensive overview of policy gradient methods and their applications in reinforcement learning.