Proximal Policy Optimization

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Proximal Policy Optimization (PPO) is a family of model-free reinforcement learning algorithms developed at OpenAI in 2017. PPO algorithms are policy gradient methods, which means that they search the space of policies rather than assigning values to state-action pairs.

PPO algorithms have some of the benefits of trust region policy optimization (TRPO) algorithms, but they are simpler to implement, more general, and have better sample complexity.^[1] It is done by using a different objective function.^[2]

See also

• Reinforcement learning
• Temporal difference learning
• Game theory

External links

• Announcement of Proximal Policy Optimization by OpenAI
• GitHub repo

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