Historical milestone · 2018
World Models
Reviewed through September 18, 2026
2018 · Historical milestone
World Models
- Era
- 2010s
- Theme
- Embodied AI & world models
- Evidence form
- Preprint + game experiments
- School / paradigm
- Model-based reinforcement learning
- Institution / context
- Google Brain; NNAISENSE
- Researchers
- David Ha; Jürgen Schmidhuber
Understand
Plain-language record, transferred from the reviewed source module.
Theory or experimental setup. Trained a visual encoder and recurrent dynamics model, then optimized a compact controller inside imagined rollouts.
Result / historical claim. Showed that a controller could learn in a compressed learned world and transfer behavior to the environment.
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. Preprint + game experiments
Limitation / debate. Evidence came from simple games, and controllers could exploit inaccuracies in the learned model.
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.