Reinforcement Learning: Policy Gradient and Actor-Critic Methods

This lecture covers the principles of reinforcement learning, focusing on policy gradient and actor-critic methods. It begins with an introduction to reinforcement learning in deep artificial neural networks, explaining the REINFORCE algorithm with baseline and the actor-critic algorithm. The instructor discusses the differences between model-based and model-free reinforcement learning, emphasizing the importance of understanding these concepts for applying reinforcement learning techniques or reading related research papers. The lecture reviews key concepts such as Q-values and V-values, and introduces eligibility traces for policy gradients. The actor-critic method is presented as a combination of policy gradient and temporal difference learning, highlighting its advantages. The session concludes with a summary of deep reinforcement learning techniques, including the use of eligibility traces and the significance of learning two neural networks: the actor and the critic. Overall, this lecture provides a comprehensive overview of advanced reinforcement learning methods, preparing students for practical applications and further study in the field.