Proximal Policy Optimization (PPO)

Introduced Proximal Policy Optimization (PPO) as an advancement over A2C, focusing on stabilizing reinforcement learning training by constraining policy updates. Explained the intuition behind PPO, the Clipped Surrogate Objective function, and the role of the probability ratio in measuring policy divergence. Provided a breakdown of the objective function's components and a practical coding example for a PPO agent.