PPO for Cart-Pole

Key Insight

Proximal Policy Optimization (PPO) is the standard on-policy reinforcement learning algorithm, balancing stable training with ease of implementation through a constrained policy gradient update. By implementing a clipping loss that limits the size of the policy update step, PPO prevents the policy from drifting into regions of parameter space that degrade performance. Solving the classic cart-pole task with PPO demonstrates how tracking the policy ratio and using an actor-critic baseline keeps updates stable without requiring complex second-order optimization.