# World Models

Authors: David Ha, Jürgen Schmidhuber.

Combines learned visual compression and recurrent dynamics with a controller, including training an agent inside a generated environment.

First public: 2018-03-27.
ArXiv initial submission: 2018-03-27.

[Original paper](https://arxiv.org/abs/1803.10122) · [Reading-list entry](https://neural-os.com/reading-list/papers/1803.10122/)
[Preprint BibTeX](https://neural-os.com/reading-list/papers/1803.10122/citation.bib) · [Preprint CSL-JSON](https://neural-os.com/reading-list/papers/1803.10122/citation.csl.json) · [JSON record](https://neural-os.com/reading-list/papers/1803.10122/record.json)

**Contribution:** Demonstrates how a compact learned environment representation can support policy learning and transfer from a simulated dream to the original task.

**Model output:** Predicted latent observations, with an image decoder and task-dependent termination predictions.

**Action interface:** Actions from the controller in the studied reinforcement-learning environments.

**State and memory:** A recurrent model summarizes the history of latent observations and actions.

**Relationship to NeuralOS:** Cited by NeuralOS as background for learned simulation and agent training.

**Scope and limitations:** The model compresses observations into a latent representation for control.

[Relationship source](https://arxiv.org/html/2507.08800v2)

Related terms: world model, recurrent dynamics, latent imagination, model-based reinforcement learning.

## Approaches

- [Interactive learned environments](https://neural-os.com/reading-list/guides/games-to-computers/): Learn how an environment changes after actions, then generate observations during continued interaction. Compare visual fidelity, control interfaces, memory, and the reliability of repeated predictions.

Reading-list version 1.0.1; literature checked through 2026-10-19.