Reinfrocement Learning

Proximal Policy Optimization (PPO) is a model-free, on-policy reinforcement learning algorithm introduced by OpenAI in 2017. It aims to improve the stability and efficiency of policy gradient methods while maintaining simplicity. Its key advantages include good performance across a wide range of tasks, ease of implementation, and compatibility with both continuous and discrete action spaces. Since its introduction, PPO has become a standard baseline in reinforcement learning research and has been successfully applied to complex problems such as training AI agents to play video games and controlling robotic systems....