A3C
E99656
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.
All labels observed (1)
| Label | Occurrences |
|---|---|
| A3C canonical | 4 |
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 |
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 ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: A3C Description of subject: 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.
Referenced by (4)
Full triples — surface form annotated when it differs from this entity's canonical label.