  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.
