Policy Gradient Methods: Direct Action Learning in Reinforcement Learning

This lecture focuses on policy gradient methods in reinforcement learning, emphasizing the direct learning of actions rather than relying on Q-values. The instructor begins by reviewing traditional TD methods and introduces the basic idea of policy gradients, which optimize actions based on rewards. The lecture discusses the log-likelihood trick for obtaining correct statistical weights and explores the advantages of policy gradient methods over Q-learning, particularly in continuous state spaces. The instructor highlights the challenges faced by TD algorithms in partially observable environments and the need for function approximation. The lecture also covers the transition from batch to online learning, illustrating how to maximize expected rewards through stochastic gradient ascent. Exercises are included to reinforce understanding of the concepts, such as calculating gradients and applying the policy gradient rule. The session concludes with a summary of the key points and a preview of upcoming topics in deep reinforcement learning, particularly the integration of policy gradients with actor-critic networks.