Policy Gradient Methods in Reinforcement Learning
Policy gradient methods are a class of techniques in reinforcement learning that focus on optimizing the policy directly. These methods have been widely used for their ability to handle high-dimensional action spaces and stochastic policies.
Popular algorithms in this category include Proximal Policy Optimization (PPO), Trust Region Policy Optimization (TRPO), and Deep Deterministic Policy Gradient (DDPG). Let's explore each of these in detail!