MuZero
E42386
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.
All labels observed (5)
How this entity was disambiguated
This entity first appeared as the object of triple T307344 — 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: MuZero Context triple: [DeepMind, developed, MuZero]
-
A.
AlphaZero
AlphaZero is a DeepMind-developed artificial intelligence system that mastered complex games like chess, shogi, and Go through self-play reinforcement learning without human-crafted strategies.
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B.
AlphaStar
AlphaStar is a DeepMind-created artificial intelligence system that achieved grandmaster-level performance in the real-time strategy game StarCraft II.
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C.
AlphaGo
AlphaGo is an artificial intelligence program developed by DeepMind that became famous for defeating world champion Go players using deep neural networks and reinforcement learning.
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D.
DeepMind
DeepMind is a leading artificial intelligence research company renowned for breakthroughs such as AlphaGo and deep reinforcement learning, operating as a subsidiary of Google.
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E.
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.
- 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: MuZero Target entity description: 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.
-
A.
AlphaZero
AlphaZero is a DeepMind-developed artificial intelligence system that mastered complex games like chess, shogi, and Go through self-play reinforcement learning without human-crafted strategies.
-
B.
AlphaStar
AlphaStar is a DeepMind-created artificial intelligence system that achieved grandmaster-level performance in the real-time strategy game StarCraft II.
-
C.
AlphaGo
AlphaGo is an artificial intelligence program developed by DeepMind that became famous for defeating world champion Go players using deep neural networks and reinforcement learning.
-
D.
DeepMind
DeepMind is a leading artificial intelligence research company renowned for breakthroughs such as AlphaGo and deep reinforcement learning, operating as a subsidiary of Google.
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E.
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.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
DeepMind algorithm
ⓘ
model-based reinforcement learning algorithm ⓘ reinforcement learning algorithm ⓘ |
| achieves |
superhuman performance in Go
ⓘ
superhuman performance in chess ⓘ superhuman performance in shogi ⓘ |
| architectureComponent |
dynamics function
ⓘ
prediction function ⓘ representation function ⓘ |
| basedOn |
Monte Carlo tree search
ⓘ
surface form:
Monte Carlo Tree Search
deep neural networks ⓘ model-based planning ⓘ |
| canPlay |
Atari 2600 games
ⓘ
Go ⓘ chess ⓘ shogi ⓘ |
| category |
game-playing AI system
ⓘ
planning algorithm ⓘ |
| comparedTo | AlphaZero ⓘ |
| countryOfOrigin | United Kingdom ⓘ |
| developer | DeepMind ⓘ |
| differenceFromAlphaZero | does not require known game rules for planning ⓘ |
| field |
artificial intelligence
ⓘ
machine learning ⓘ reinforcement learning ⓘ |
| handles | discrete action spaces ⓘ |
| inputType | raw observations such as images ⓘ |
| inspiredBy |
AlphaGo
ⓘ
AlphaGo Zero ⓘ AlphaZero ⓘ |
| keyFeature |
does not require prior knowledge of game rules
ⓘ
learns environment dynamics from data ⓘ plans using a learned model ⓘ searches in latent state space ⓘ uses value, policy, and reward prediction ⓘ |
| learningSignal | game outcomes ⓘ |
| notableFor |
planning with a learned model without access to true environment dynamics
ⓘ
state-of-the-art performance on Atari benchmark at time of publication ⓘ |
| optimizationObjective | maximize expected cumulative reward ⓘ |
| organization |
DeepMind
ⓘ
surface form:
Google DeepMind
|
| outperforms | prior model-free algorithms on Atari ⓘ |
| publicationYear | 2019 ⓘ |
| publishedIn | Nature ⓘ |
| titleOfPaper |
MuZero
self-linksurface differs
ⓘ
surface form:
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
|
| trainingMethod |
reinforcement learning
ⓘ
self-play ⓘ |
| uses | gradient-based optimization ⓘ |
| usesAlgorithm |
Monte Carlo tree search
ⓘ
surface form:
Monte Carlo Tree Search
|
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: MuZero Description of subject: 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.
Referenced by (10)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
Mastering the game of Go without human knowledge
this entity surface form:
Mastering Atari, Go, chess and shogi by planning with a learned model
this entity surface form:
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
this entity surface form:
Mastering Atari, Go, Chess and Shogi by planning with a learned model