Proximal Policy Optimization (PPO) is a reinforcement learning algorithm used to train agents to make decisions within environments. At its core, PPO is designed to optimize an agent's policy, which is a strategy for choosing actions based on the current state. One of the main features of PPO is that it allows for large updates to the policy while maintaining a close relationship to the previous policy. This is achieved through the use of a clipping mechanism that controls how much the policy can change in each update, preventing drastic shifts that could destabilize training.
The algorithm works by sampling a set of actions based on the current policy and collecting rewards based on those actions. After gathering this data, PPO uses it to update the policy in a way that maximizes expected rewards. Notably, PPO uses a surrogate objective function, which is a mathematical representation of the rewards, to evaluate modifications to the policy. The clipping aspect of the objective function limits how much the new policy can diverge from the existing one, effectively keeping updates in a "trust region." This is crucial because it stabilizes learning, allowing the algorithm to make more reliable progress towards optimal behavior.
An example of how PPO is applied can be seen in training agents for complex tasks like playing video games or robotic control. In these cases, PPO frequently collects batches of experiences over multiple episodes, updating the policy after accumulating enough data. This batch processing allows the algorithm to balance exploration and exploitation effectively. By using PPO, developers can achieve good performance with fewer fine-tuning efforts, making it a popular choice in various reinforcement learning applications. Overall, PPO combines efficiency with stability, making it a practical option for many learning tasks.