Policy Gradient with PyTorch

This post introduces Policy Gradient methods as an alternative to value-based methods like Q-Learning. It explains the core concepts of policy gradients, their advantages (stochastic policies, continuous action spaces) and disadvantages (local optima, variance). The post details the REINFORCE algorithm, a Monte Carlo Policy Gradient method, and outlines its update mechanism using gradient ascent to maximize the expected return. It also provides links to a PyTorch implementation tutorial and relevant environments (CartPole-v1, PixelCopter, Pong).