Proximal Policy Optimization

Proximal Policy Optimization (PPO) is a popular reinforcement learning algorithm designed to optimize policies in a stable and efficient manner. It operates by updating the policy based on the ratio of the new and old probabilities of actions taken, ensuring that updates do not deviate too far from the previous policy. This approach helps to maintain a balance between exploration and exploitation, allowing for effective learning in complex environments. PPO is widely used in various applications, including robotics, game playing, and autonomous systems, due to its simplicity and effectiveness in handling continuous action spaces.