Historical milestone · 2025
DreamerV3
Reviewed through September 18, 2026
2025 · Historical milestone
DreamerV3
- Era
- 2020s
- Theme
- Embodied AI & world models
- Evidence form
- Peer-reviewed cross-domain experiments
- School / paradigm
- Model-based reinforcement learning
- Institution / context
- Google DeepMind
- Researchers
- Danijar Hafner; collaborators
Understand
Plain-language record, transferred from the reviewed source module.
Theory or experimental setup. Learned a latent world model and actor-critic from experience with one configuration across more than 150 tasks.
Result / historical claim. Reported broad benchmark performance and learning to obtain diamonds in Minecraft from pixels and actions.
Apply
Professional implication, only where the reviewed record states one.
The checked-in record does not state a separate professional application for this entry. The topic page places it in the wider research lineage: Embodied AI and world models.
Verify
Evidence status, stated limitations, and the external sources this record actually carries.
Evidence form. Peer-reviewed cross-domain experiments
Limitation / debate. Much evidence remained simulation-heavy; real-world sensing, safety, and sample costs were unresolved.
Source status. The source link below is the verified link our reviewed topic research already carries for this milestone.
Reproduce
A reproduction tutorial is linked only when one exists for this exact record.
A reproduction tutorial is not yet available for this entry. The closest reviewed material is Embodied AI and world models.
Cite or share
APA-like: This historical record carries a year only, and no author or publisher of record in the checked-in data. An APA reference would have to invent that metadata.
BibTeX: BibTeX requires an author and publication venue. Historical lineage entries store a narrative record and its source link, not structured authorship, so the field would be fabricated.