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.

Try in SPARQL Jump to: Surface forms Disambiguation Statements Elicitation Referenced by

AI illustration

All labels observed (1)

Label	Occurrences
A3C canonical	9

How this entity was disambiguated

This entity first appeared as the object of triple T824087 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.

NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07

Target entity: A3C
Context triple: [OpenAI Baselines, implementsAlgorithm, A3C]

A. A2C
A2C (Advantage Actor-Critic) is a popular synchronous policy gradient reinforcement learning algorithm that combines value-based and policy-based methods to improve training stability and efficiency.
B. ACO
ACO is the commonly used abbreviation for Allied Command Operations, one of NATO’s two strategic military commands responsible for planning and executing alliance operations.
C. AAC
AAC is a leading peer-reviewed scientific journal that publishes research on antimicrobial agents, chemotherapy, and related aspects of infectious diseases.
D. AAC
AAC (Advanced Audio Coding) is a widely used digital audio compression format known for delivering better sound quality than MP3 at similar bit rates and is commonly used in online music stores and streaming services.
E. A35
A35 is a major French motorway in the Alsace region that runs north–south near the German border, connecting cities such as Strasbourg and Mulhouse.
F. None of above. chosen
G. Unsure - the case is ambiguous/there is not enough information to decide.

NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07

Target entity: A3C
Target entity description: 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.

A. A2C
A2C (Advantage Actor-Critic) is a popular synchronous policy gradient reinforcement learning algorithm that combines value-based and policy-based methods to improve training stability and efficiency.
B. ACO
ACO is the commonly used abbreviation for Allied Command Operations, one of NATO’s two strategic military commands responsible for planning and executing alliance operations.
C. AAC
AAC (Advanced Audio Coding) is a widely used digital audio compression format known for delivering better sound quality than MP3 at similar bit rates and is commonly used in online music stores and streaming services.
D. AAC
AAC is a leading peer-reviewed scientific journal that publishes research on antimicrobial agents, chemotherapy, and related aspects of infectious diseases.
E. A35
A35 is a major French motorway in the Alsace region that runs north–south near the German border, connecting cities such as Strasbourg and Mulhouse.
F. None of above. chosen

Statements (51)

Predicate	Object
instanceOf	reinforcement learning algorithm ⓘ
abbreviationOf	Asynchronous Advantage Actor-Critic ⓘ
canUseNetworkType	convolutional neural networks ⓘ recurrent neural networks ⓘ
comparedWith	DQN ⓘ linked to: Atari deep Q-network
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 games ⓘ linked to: Atari 2600 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 ⓘ

How these facts were elicited

Referenced by (9)

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 ⓘ

Asynchronous Advantage Actor-Critic → abbreviation → A3C ⓘ

Asynchronous Methods for Deep Reinforcement Learning → abbreviation → A3C ⓘ

IMPALA → comparedWith → A3C ⓘ

Actor-Critic using Kronecker-Factored Trust Region → relatedTo → A3C ⓘ

Tianshou → supportsAlgorithm → A3C ⓘ

All labels observed (1)

How this entity was disambiguated Show

Statements (51)

How these facts were elicited Show

Referenced by (9)

How this entity was disambiguated

How these facts were elicited