reinforcement learning
MuZero
Agent that plans with a learned model and mastered Atari, Go, chess and shogi without being given the rules. Paper by Schrittwieser et al. (2019).
Levelmodel
Classreinforcement learning
Parametersnot in the notes
Contextnot in the notes
Released19 Nov 2019
Opennessunknown
Runs onnot in the notes
Statusnot in the notes
Lineage
successor
AlphaZero is the parent (successor)medium confidencenotesWhere this comes from
Parent and child relations from model cards, Hugging Face base_model fields and the genealogy tables of the research notes. The tree shows two generations each way, 2 models. Confidence and evidence are the notes' own.
R&D Topics
Identity
- Developer
- Google DeepMind
- Family
- not in the notes
- Released
- 19 Nov 2019 sourcesannouncement page
- Weights published
- –
- Record checked
- 12 Oct 2026
Where this comes from
Developer and dates from the research notes. Release date is the vendor's public announcement; weights date is when open weights were published.
Architecture and Size
- Architecture
- other
- Total parameters
- – sources
- Active parameters
- –
- Checkpoint parameters
- –
- Context length
- not in the notes
- Input
- not in the notes
- Output
- action
Where this comes from
Parameter counts as the developer states them. Where the checkpoint holds a different total, both are shown (register A16).
Openness and Access
- Openness
- unknown
- Licence
- not in the notes sources
- Access routes
- not in the notes
Where this comes from
The licence file governs over registry tags and aggregator labels.
Deployment
- Runs on
- not in the notes
- Footprint
- model_class left empty: AURION has no reinforcement learning class yet.
- Runtimes
- not in the notes
Where this comes from
Deployment tiers and runtimes as named in the notes. Footprint is the notes' figure, not a measurement by AURION.
Indonesia
- Indonesian support
- not stated
- Regional languages
- not in the notes
- Jakarta hosting
- unknown
- Languages
- not in the notes
Where this comes from
Indonesia fields are carried on every model; unknown is an allowed value.
Benchmark Results
The notes record no benchmark result for this model.
Industry Use Cases
No industry use case links this model.
Links
ModelMuZeroGoogle DeepMind, reinforcement learning
MuZeroGoogle DeepMind, reinforcement learningR&D TopicsSelf-Play Reinforcement Learning with SearchModel-Based Reinforcement Learning with Learned World Models
Use Casesnone recorded
Who Runs Itnone recorded
Organisations 1
R&D topics 2
Self-Play Reinforcement Learning with SearchAgents that reach superhuman play by playing against themselves at scale, often with tree…exemplarModel-Based Reinforcement Learning with Learned World ModelsAgents that learn a model of their environment and improve by planning or imagining insid…exemplar
Sources
- arxiv.org/abs/1911.08265 https://arxiv.org/abs/1911.08265
To cite this page: Project AURION, AI Use-Cases Research & Innovation, "MuZero", https://aurion.semai.id/models/google-deepmind-muzero/, records of October 2026.