Policy Gradient Methods are a class of algorithms in reinforcement learning that optimize the policy directly. Instead of estimating the value of actions, these methods adjust the policy parameters to maximize the expected reward. This is done by calculating the gradient of the expected reward with respect to the policy parameters and updating them in the direction that increases the reward. These methods are particularly useful in environments with high-dimensional action spaces or when the action space is continuous. By focusing on the policy itself, Policy Gradient Methods can effectively learn complex behaviors and strategies, making them suitable for various applications, including robotics and game playing.