Proximal Policy Optimization (PPO) is a reinforcement learning algorithm designed to train agents by optimizing their policies—the strategies they use to make decisions. Unlike earlier methods that could become unstable when policies change too drastically, PPO limits the size of policy updates to ensure steady improvement. It does this by using a clipped objective function that prevents new policies from deviating too far from older versions. For example, if an agent learns to balance a pole on a cart, PPO ensures that each update doesn’t radically alter the balancing strategy, avoiding sudden failures caused by overly aggressive changes.
The core mechanism of PPO involves calculating the ratio of the new policy’s action probability to the old policy’s probability. This ratio is multiplied by an advantage estimate (a measure of how much better an action is compared to average) to form the objective. However, PPO clips this ratio within a narrow range, such as [0.8, 1.2], to bound policy changes. If the ratio exceeds these limits, the algorithm uses the clipped value instead, effectively ignoring updates that would push the policy too far. For instance, in a robotics task where precise movements are critical, clipping prevents a policy from suddenly favoring extreme motions that could destabilize the system. Additionally, PPO optimizes this clipped objective over multiple epochs using minibatches of data, reusing collected experience efficiently without causing destructive updates.
PPO is popular because it balances performance and simplicity. Unlike Trust Region Policy Optimization (TRPO), which requires complex optimization techniques, PPO can be implemented with standard gradient descent. Developers often pair it with a value network that estimates expected rewards, enabling advantage calculations. PPO also incorporates entropy regularization to encourage exploration, preventing policies from becoming too deterministic. For example, in training a game-playing AI, entropy ensures the agent occasionally tries unconventional moves, avoiding local optima. These features make PPO versatile for tasks like robotic control, game AI, or simulation training, where reliability and ease of implementation are critical. Its adaptability and robustness explain its widespread adoption in both research and industry applications.