Mini toolsDefinition
A policy gradient method that constrains policy updates to prevent destructively large changes.
Detailed Explanation
PPO improves on standard policy gradient methods by clipping the objective function to ensure policy updates aren't too large. This prevents catastrophic policy degradation and makes training more stable. It alternates between sampling data through interaction with the environment and optimizing a surrogate objective function.
Use Cases
Robot learning, game AI, autonomous systems, continuous control tasks
