How does the Proximal Policy Optimization (PPO) algorithm work in reinforcement learning?

Proximal Policy Optimization (PPO) is a reinforcement learning algorithm designed to balance ease of implementation with stable performance. It belongs to the policy gradient family, which directly optimizes the policy (the agent’s behavior) by estimating the gradient of expected rewards. Unlike earlier methods like Trust Region Policy Optimization (TRPO), which use complex constraints, PPO simplifies training by limiting policy updates through a clipped objective function. The core idea is to prevent large, destabilizing changes to the policy during updates by ensuring the new policy stays “proximal” (close) to the old one. This is done by modifying the objective function to penalize updates that would cause the policy to deviate too far from its previous version.

PPO works by iteratively collecting data through interactions with the environment and using that data to update the policy. Each update step involves two key components: a clipped surrogate objective and advantage estimation. The clipped objective compares the action probabilities of the new policy to the old policy, calculating a ratio of these probabilities. If this ratio deviates beyond a predefined range (e.g., 0.8 to 1.2), the objective function is clipped to limit the gradient step. For example, if the ratio suggests a 50% increase in the likelihood of taking a good action, PPO might cap this ratio at 20% to avoid overcommitting to a single update. Advantages, which estimate how much better an action is than average, are computed using methods like Generalized Advantage Estimation (GAE) to reduce variance in updates.

In practice, PPO is implemented with an actor-critic architecture. The actor (policy) selects actions, while the critic (value function) estimates state values to compute advantages. Developers typically use multiple epochs of minibatch updates on the same dataset to improve sample efficiency. For example, after collecting 1,000 timesteps of data, PPO might perform 4-5 passes over small batches (e.g., 64 samples) to update the policy. Common hyperparameters include a clipping threshold (ε) of 0.1–0.3, a learning rate of 3e-4, and a discount factor (γ) of 0.99. PPO’s strength lies in its simplicity: the clipping mechanism avoids complex constraints, and the use of multiple epochs makes it data-efficient. This has made it a popular choice for tasks ranging from robotic control to game AI, where stable and sample-efficient training is critical.