Natural Policy Gradients In Reinforcement Learning Explained
This provides a foundational explanation for researchers and practitioners in reinforcement learning, but it is incremental as it clarifies existing concepts rather than introducing new ones.
The paper tackles the problem of slow convergence in traditional policy gradient methods in reinforcement learning by explaining natural policy gradients, which converge quicker and better and form the foundation of modern methods like TRPO and PPO.
Traditional policy gradient methods are fundamentally flawed. Natural gradients converge quicker and better, forming the foundation of contemporary Reinforcement Learning such as Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO). This lecture note aims to clarify the intuition behind natural policy gradients, focusing on the thought process and the key mathematical constructs.