Policy Gradient Methods

Policy Gradient Methods are a reinforcement learning technique that directly optimizes the policy function to maximize long-term rewards. The goal is to find the optimal policy in a given environment, enabling the agent to select the best action based on the current state. This method has significant advantages in handling high-dimensional action spaces and continuous action tasks, and is widely applied in areas such as robotics control, game AI, and complex decision-making systems, effectively enhancing the performance and adaptability of these systems.