Policy Gradient Methods in Reinforcement Learning

This lecture focuses on policy gradient methods within the context of reinforcement learning. It begins with an overview of reinforcement learning approaches, contrasting value-based and policy-based methods. The instructor discusses the optimization formulation for policy-based methods, emphasizing the importance of parameterizing policies for both discrete and continuous actions. Various parameterization techniques, including softmax and neural networks, are introduced. The lecture then delves into the policy gradient method, explaining how to compute gradients using stochastic estimates and the significance of unbiased gradient estimators. The instructor highlights the challenges of high variance in policy gradient methods and introduces techniques to reduce this variance, such as using baseline functions. The lecture concludes with practical examples, including the application of policy gradient methods to the cartpole problem, illustrating how these methods can effectively learn to balance the pole. Overall, the lecture provides a comprehensive understanding of policy gradient methods and their applications in reinforcement learning.