# Action-Conditional Video Prediction using Deep Networks in Atari Games

Authors: Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard Lewis, Satinder Singh.

Learns to predict Atari frames from past observations and control inputs, connecting visual generation to an agent's actions.

First public: 2015-07-31.
ArXiv initial submission: 2015-07-31.

[Original paper](https://arxiv.org/abs/1507.08750) · [Reading-list entry](https://neural-os.com/reading-list/papers/1507.08750/)
[Preprint BibTeX](https://neural-os.com/reading-list/papers/1507.08750/citation.bib) · [Preprint CSL-JSON](https://neural-os.com/reading-list/papers/1507.08750/citation.csl.json) · [JSON record](https://neural-os.com/reading-list/papers/1507.08750/record.json)

**Contribution:** Develops feedforward and recurrent architectures with action-dependent transformations and evaluates long visual rollouts and their usefulness for control.

**Model output:** Predicted game frames.

**Action interface:** Discrete Atari control actions.

**State and memory:** Past observations; the recurrent variant carries a hidden state through time.

**Relationship to NeuralOS:** Earlier action-conditioned visual-prediction work cited in NeuralOS's introduction and references.

**Scope and limitations:** The experiments use Atari games.

[Relationship source](https://arxiv.org/html/2507.08800v1)

Related terms: action-conditioned video prediction, visual dynamics, Atari, learned simulator.

## 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.