Proximal Policy Optimization (PPO) is a family of
model-free reinforcement learning
Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine ...
algorithms developed at
OpenAI
OpenAI is an artificial intelligence (AI) research laboratory consisting of the for-profit corporation OpenAI LP and its parent company, the non-profit OpenAI Inc. The company conducts research in the field of AI with the stated goal of promo ...
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. It is done by using a different objective function.
See also
*
Reinforcement learning
Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine ...
*
Temporal difference learning
Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate of the value function. These methods sample from the environment, like Monte Carlo methods, ...
*
Game theory
References
External links
Announcement of Proximal Policy Optimization by OpenAIGitHub repo
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Machine learning algorithms
Reinforcement learning