Proximal Policy Optimisation

Proximal Policy Optimisation (PPO) is a widely adopted on-policy reinforcement learning algorithm that belongs to the family of policy gradient methods. It was introduced as an improvement over Trust Region Policy Optimization (TRPO), aiming to achieve similar performance guarantees with significantly simpler implementation. The core mechanism of PPO involves optimizing a 'clipped' surrogate objective function. This objective encourages policy updates that do not deviate too far from the previous policy, using a ratio of new to old policy probabilities. The crucial clipping mechanism prevents excessively large or destructive policy updates that could destabilize training and lead to performance collapse. PPO is highly valued for its excellent balance of sample efficiency, robust performance, and ease of implementation. It effectively addresses the challenge of finding stable policy updates in complex environments. Consequently, PPO is extensively used across various domains, including robotics control, game AI (e.g., OpenAI Five, AlphaStar), autonomous systems, and general reinforcement learning research, serving as a strong baseline for many tasks.