Proximal Policy Optimization Explained

Proximal Policy Optimization (PPO) is a reinforcement learning algorithm developed by OpenAI that improves training stability through a clipped surrogate objective function. The article explains PPO's mathematical foundations, including the policy ratio and advantage estimation, and demonstrates implementation in PyTorch using an actor-critic architecture on CartPole. It compares PPO with alternatives like DQN, A2C, and TRPO, highlighting PPO's balance of simplicity and performance across discrete and continuous action spaces. The guide covers practical applications in robotics, gaming, and language model fine-tuning, along with hyperparameter tuning recommendations and common pitfalls to avoid during training.