Proximal Policy Optimization

Proximal Policy Optimization (PPO) is a type of reinforcement learning algorithm used to train agents to perform tasks by learning from interactions with their environment. It is designed to optimize policy-based models, which dictate the actions an agent takes in given states. PPO improves upon earlier methods by incorporating a clipped objective function that restricts the degree to which the policy can change at each update. This approach helps to maintain a balance between exploration and exploitation while ensuring stable learning. The key advantage of PPO is its efficiency and simplicity, allowing for robust training of neural networks in complex environments by making incremental updates to the policy. It is widely used in various applications, including robotics, game playing, and simulated environments.