Reinforcement Learning Policy Optimization: From Policy Gradients to PPO

Reinforcement learning policy optimization has evolved from REINFORCE and Actor-Critic to TRPO and then PPO, with the core goal of making policy gradient updates more stable and efficient. PPO uses clipping on the probability ratio between the new and old policies to limit the size of each update, balancing implementation complexity and stability, and is suitable for both continuous and discrete actions. The article also reviews GAE advantage estimation, the on-policy data flow, and tuning approaches for key hyperparameters such as learning rate and clip range.