Proximal Policy Optimization (PPO)

Proximal Policy Optimization (PPO) is a method used in reinforcement learning, where an agent learns to make decisions by interacting with an environment. It optimizes the agent's behavior, or "policy," while ensuring that changes to this policy are not too drastic. This balance prevents the learning process from becoming unstable. PPO uses collected data from the agent's experiences to gradually improve its actions based on feedback, aiming to maximize rewards over time. In essence, it’s a way for machines to learn optimal behaviors through trial and error, similar to how humans refine skills.