Learning General Policies with Policy Gradient Methods

Policy gradient methods are a cornerstone of reinforcement learning (RL), enabling agents to learn optimal behaviors in complex environments. This paper investigates advances in policy gradient methods aimed at learning more generalizable policies. It explores techniques to improve sample efficiency and stability, crucial for deploying RL agents in real-world applications such as robotics, autonomous driving, and game AI. The research focuses on developing robust algorithms that can effectively handle various state and action spaces, facilitating the creation of adaptable AI systems capable of performing diverse tasks efficiently.