TLDR: Proximal Policy Optimization (PPO) is a type of reinforcement learning algorithm that helps computers learn how to make decisions by trying out different strategies and seeing which ones work best.

Proximal Policy Optimization (PPO) is a family of model-free reinforcement learning algorithms developed by OpenAI. These algorithms are designed to help computers learn how to make decisions in complex environments. Instead of explicitly programming the computer with a set of rules, PPO allows the computer to learn through trial and error.

PPO algorithms are part of a larger field called machine learning, which is all about teaching computers to learn from data and improve their performance over time. In particular, PPO algorithms are a type of policy gradient method, which means that they search the space of policies (strategies) rather than assigning values to state-action pairs.

One of the advantages of PPO algorithms is that they have some of the benefits of trust region policy optimization (TRPO) algorithms, but they are simpler to implement and have better sample complexity. This means that PPO algorithms can learn more efficiently from a smaller amount of data.

In summary, Proximal Policy Optimization (PPO) is a type of reinforcement learning algorithm that helps computers learn how to make decisions by trying out different strategies and seeing which ones work best. It is a powerful tool in the field of machine learning and has been used in a wide range of applications, from robotics to game playing.

Related Links:

• Announcement of Proximal Policy Optimization by OpenAI
• GitHub repo