Proximal Policy Optimization

Proximal Policy Optimization (PPO) is a method used in reinforcement learning, a branch of artificial intelligence. It helps an agent learn to make decisions by balancing exploration and exploitation. Think of it as training a dog: you reward good behavior while gently correcting mistakes. PPO updates the agent's strategies based on feedback while ensuring changes aren’t too drastic, which helps maintain stability and prevents erratic behavior. This way, the agent gradually improves its performance in complex tasks, like playing games or navigating environments, by optimizing its actions over time.