Proximal Policy Optimization (PPO) is a popular reinforcement learning algorithm developed by OpenAI. It is part of a family of policy gradient methods and is designed to improve the stability and performance of training policies in environments where agents learn to make decisions. PPO is notable for its balance between simplicity and effectiveness.

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