# Learning to Simulate Dynamic Environments with GameGAN

Authors: Seung Wook Kim, Yuhao Zhou, Jonah Philion, Antonio Torralba, Sanja Fidler.

Learns an interactive visual game simulator from gameplay and keyboard actions using a generative adversarial network.

First public: 2020-05-25.
ArXiv initial submission: 2020-05-25.

[Original paper](https://arxiv.org/abs/2005.12126) · [Reading-list entry](https://neural-os.com/reading-list/papers/2005.12126/)
[Preprint BibTeX](https://neural-os.com/reading-list/papers/2005.12126/citation.bib) · [Preprint CSL-JSON](https://neural-os.com/reading-list/papers/2005.12126/citation.csl.json) · [JSON record](https://neural-os.com/reading-list/papers/2005.12126/record.json)

**Contribution:** Introduces a memory module for revisited locations and separates static and dynamic image components, enabling visual consistency and component recombination.

**Model output:** The next game-screen image.

**Action interface:** Game controls represented by keyboard actions.

**State and memory:** Recurrent dynamics plus an external memory module that stores an internal environmental map.

**Relationship to NeuralOS:** An earlier interactive neural simulator discussed in NeuralOS's related work.

**Scope and limitations:** The simulator learns from recorded games, with memory organized as a spatial map.

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

Related terms: GameGAN, neural game engine, interactive simulation, visual memory.

## 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.