Arcade Learning Environment
E95193
Arcade Learning Environment is a widely used research platform that provides a suite of Atari 2600 games for developing and evaluating reinforcement learning algorithms.
All labels observed (2)
| Label | Occurrences |
|---|---|
| Arcade Learning Environment canonical | 6 |
| Atari multi-agent environments | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T805160 — 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: Arcade Learning Environment Context triple: [OpenAI Gym, influencedBy, Arcade Learning Environment]
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A.
Atari deep Q-network
The Atari deep Q-network is a pioneering deep reinforcement learning system that learned to play a wide range of Atari 2600 video games directly from raw pixels at human-level or better performance.
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B.
OpenAI Baselines
OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms released by OpenAI for research and benchmarking.
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C.
OpenAI Gym
OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms through a standardized collection of environments and interfaces.
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D.
MuZero
MuZero is a DeepMind reinforcement learning algorithm that learns to plan and master complex games like Go, chess, and Atari without being given the rules in advance.
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E.
"A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence"
"A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence" is the seminal 1955 research proposal by John McCarthy and colleagues that launched the field of artificial intelligence by defining its goals and organizing the landmark 1956 Dartmouth conference.
- 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: Arcade Learning Environment Target entity description: Arcade Learning Environment is a widely used research platform that provides a suite of Atari 2600 games for developing and evaluating reinforcement learning algorithms.
-
A.
Atari deep Q-network
The Atari deep Q-network is a pioneering deep reinforcement learning system that learned to play a wide range of Atari 2600 video games directly from raw pixels at human-level or better performance.
-
B.
OpenAI Baselines
OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms released by OpenAI for research and benchmarking.
-
C.
OpenAI Gym
OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms through a standardized collection of environments and interfaces.
-
D.
MuZero
MuZero is a DeepMind reinforcement learning algorithm that learns to plan and master complex games like Go, chess, and Atari without being given the rules in advance.
-
E.
"A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence"
"A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence" is the seminal 1955 research proposal by John McCarthy and colleagues that launched the field of artificial intelligence by defining its goals and organizing the landmark 1956 Dartmouth conference.
- F. None of above. chosen
Statements (46)
| Predicate | Object |
|---|---|
| instanceOf |
benchmark suite
ⓘ
reinforcement learning environment collection ⓘ research platform ⓘ software framework ⓘ |
| actionSpace | finite set of joystick and button actions ⓘ |
| benchmarkLevel | classic control from pixels ⓘ |
| category |
Atari-based RL testbed
ⓘ
RL benchmark environment ⓘ |
| domain |
artificial intelligence research
ⓘ
reinforcement learning ⓘ |
| evaluationMetric |
average return over episodes
ⓘ
game score ⓘ |
| fullName | Arcade Learning Environment ⓘ |
| gamePlatform | Atari 2600 ⓘ |
| goal |
enable fair comparison of RL algorithms
ⓘ
facilitate reproducible RL research ⓘ |
| implementationLanguage |
C++
ⓘ
Python bindings ⓘ |
| includes | multiple Atari 2600 games ⓘ |
| inputType | raw game screen images ⓘ |
| inspired | development of standardized RL benchmarks ⓘ |
| license | open-source license ⓘ |
| notableUse |
benchmarking policy-based RL algorithms
ⓘ
benchmarking value-based RL algorithms ⓘ evaluation of Deep Q-Networks (DQN) ⓘ evaluation of deep reinforcement learning methods ⓘ |
| observationSpace | high-dimensional visual observations ⓘ |
| outputType |
game score rewards
ⓘ
terminal game states ⓘ |
| platformType | Atari 2600 game platform emulator ⓘ |
| provides |
standardized interface to Atari 2600 games
ⓘ
suite of Atari 2600 games ⓘ |
| requires | Atari 2600 emulator ⓘ |
| researchCommunity |
machine learning community
ⓘ
reinforcement learning community ⓘ |
| shortName | ALE ⓘ |
| supports |
discrete action spaces
ⓘ
episodic tasks ⓘ visual observation spaces ⓘ |
| taskType | single-agent reinforcement learning tasks ⓘ |
| usedBy |
academic researchers
ⓘ
industry research labs ⓘ |
| usedFor |
benchmarking reinforcement learning agents
ⓘ
comparing performance of RL algorithms ⓘ developing reinforcement learning algorithms ⓘ evaluating reinforcement learning algorithms ⓘ |
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: Arcade Learning Environment Description of subject: Arcade Learning Environment is a widely used research platform that provides a suite of Atari 2600 games for developing and evaluating reinforcement learning algorithms.
Referenced by (7)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: Arcade Learning Environment
Asynchronous Methods for Deep Reinforcement Learning
→
benchmarkDomain
→
Arcade Learning Environment
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