MuZero

E42386

DeepMind algorithm model-based reinforcement learning algorithm reinforcement learning algorithm

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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All labels observed (5)

Label	Occurrences
MuZero canonical	6
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model	1
Mastering Atari, Go, Chess and Shogi by planning with a learned model	1
Mastering Atari, Go, chess and shogi by planning with a learned model	1
Mastering the game of Go without human knowledge	1

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.
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.
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.
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 ⓘ linked to: 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	Google DeepMind ⓘ linked to: DeepMind
outperforms	prior model-free algorithms on Atari ⓘ
publicationYear	2019 ⓘ
publishedIn	Nature ⓘ
titleOfPaper	Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model ⓘ linked to: MuZero
trainingMethod	reinforcement learning ⓘ self-play ⓘ
uses	gradient-based optimization ⓘ
usesAlgorithm	Monte Carlo Tree Search ⓘ linked to: Monte Carlo tree search

How these facts were elicited

Referenced by (10)

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

DeepMind → developed → MuZero ⓘ

AlphaGo → successor → MuZero ⓘ

AlphaGo → inspired → MuZero ⓘ

David Silver → knownFor → MuZero ⓘ

David Silver → notableWork → MuZero ⓘ

David Silver → notablePaper → Mastering the game of Go without human knowledge ⓘ

linked to: MuZero

David Silver → notablePaper → Mastering Atari, Go, chess and shogi by planning with a learned model ⓘ

linked to: MuZero

MuZero → titleOfPaper → Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model ⓘ

linked to: MuZero

Ioannis Antonoglou → knownFor → MuZero ⓘ

Ioannis Antonoglou → coAuthorOf → Mastering Atari, Go, Chess and Shogi by planning with a learned model ⓘ

linked to: MuZero

All labels observed (5)

How this entity was disambiguated Show

Statements (48)

How these facts were elicited Show

Referenced by (10)

How this entity was disambiguated

How these facts were elicited