A3C

E99656

reinforcement learning algorithm

A3C (Asynchronous Advantage Actor-Critic) is a reinforcement learning algorithm that trains multiple parallel agents to learn policies and value functions efficiently using asynchronous gradient updates.

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All labels observed (1)

Label	Occurrences
A3C canonical	4

Statements (51)

Predicate	Object
instanceOf	reinforcement learning algorithm ⓘ
abbreviationOf	Asynchronous Advantage Actor-Critic ⓘ
canUseNetworkType	convolutional neural networks ⓘ recurrent neural networks ⓘ
comparedWith	Atari deep Q-network ⓘ surface form: DQN
developedAtOrganization	DeepMind ⓘ
fullName	Asynchronous Advantage Actor-Critic ⓘ
handlesInputType	high-dimensional sensory input ⓘ raw pixel observations ⓘ
hasLearningParadigm	model-free reinforcement learning ⓘ
hasLearningType	actor-critic method ⓘ policy gradient method ⓘ
hasProperty	does not require experience replay ⓘ efficient use of multi-core CPUs ⓘ improves training stability via parallelism ⓘ
inspiredAlgorithms	A2C ⓘ ACKTR ⓘ IMPALA ⓘ
introducedBy	Adrià Puigdomènech Badia ⓘ Alex Graves ⓘ David Silver ⓘ Koray Kavukcuoglu ⓘ Mehdi Mirza ⓘ Tim Harley ⓘ Timothy P. Lillicrap ⓘ Volodymyr Mnih ⓘ
introducedInPaper	Asynchronous Methods for Deep Reinforcement Learning ⓘ
introducedInYear	2016 ⓘ
isOnPolicy	true ⓘ
optimizationObjective	maximize expected cumulative reward ⓘ
optimizationStyle	asynchronous gradient descent ⓘ
reducesVariance	policy gradient estimates ⓘ
supportsParallelism	true ⓘ
targetDomain	Atari 2600 ⓘ surface form: Atari 2600 games continuous control problems ⓘ control tasks ⓘ
trainingSignalType	bootstrapped returns ⓘ
usesArchitecture	actor-critic architecture ⓘ
usesBaseline	state-value function ⓘ
usesComponent	advantage function ⓘ policy network ⓘ value network ⓘ
usesExplorationMethod	on-policy exploration ⓘ
usesLossComponent	entropy regularization ⓘ policy loss ⓘ value loss ⓘ
usesNeuralNetworks	deep neural networks ⓘ
usesParallelAgents	multiple parallel workers ⓘ
usesSignal	advantage estimate ⓘ
usesTrainingMode	asynchronous training ⓘ
usesUpdateScheme	asynchronous gradient updates ⓘ

Referenced by (4)

Full triples — surface form annotated when it differs from this entity's canonical label.

OpenAI Baselines → implementsAlgorithm → A3C ⓘ

ACKTR → comparedWith → A3C ⓘ

PPO → relatedTo → A3C ⓘ

Generalized Advantage Estimation → compatibleWith → A3C ⓘ