PPO

E98478

reinforcement learning algorithm

PPO (Proximal Policy Optimization) is a popular reinforcement learning algorithm known for its stability and sample efficiency in training complex policies, especially in continuous control and high-dimensional environments.

Statements (49)

Predicate	Object
instanceOf	reinforcement learning algorithm →
abbreviationFor	Proximal Policy Optimization NERFINISHED →
aimsFor	sample efficiency → stable policy updates →
commonlyUsedIn	MuJoCo control tasks → OpenAI Gym benchmarks → game playing → robotics control →
designedFor	complex policies → continuous control tasks → high-dimensional environments →
developedBy	OpenAI NERFINISHED →
fullName	Proximal Policy Optimization NERFINISHED →
hasVariant	PPO-Clip NERFINISHED → PPO-Penalty →
implementedIn	PyTorch RL libraries → RLlib NERFINISHED → Stable-Baselines3 NERFINISHED → TensorFlow Agents NERFINISHED →
improvesUpon	TRPO NERFINISHED →
introducedInPaper	Proximal Policy Optimization Algorithms NERFINISHED →
keyIdea	approximates trust region methods without complex constraints → constrains policy updates to be proximal to the old policy → uses clipped surrogate objective →
objectiveIncludes	entropy bonus (in many implementations) →
oftenCombinedWith	GAE NERFINISHED → advantage estimation →
optimizationType	on-policy → policy gradient →
primaryAuthors	Alec Radford NERFINISHED → Filip Wolski NERFINISHED → John Schulman NERFINISHED → Oleg Klimov NERFINISHED → Prafulla Dhariwal NERFINISHED →
property	relatively easy to implement → robust to hyperparameter choices → widely adopted as a default RL baseline →
publicationYear	2017 →
relatedTo	A2C NERFINISHED → A3C NERFINISHED → TRPO NERFINISHED →
supports	continuous action spaces → discrete action spaces →
trainingStyle	mini-batch updates → multiple epochs over collected trajectories →
uses	clipping parameter epsilon → importance sampling ratio → stochastic gradient ascent → surrogate objective function →

Referenced by (3)

Subject (surface form when different)	Predicate
OpenAI Baselines →	implementsAlgorithm
Stable Baselines →	supportsAlgorithm
TF-Agents →	supportsAlgorithmFamily