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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Reinforcement Learning environments based on the 1993 game Doom :godmode:
| Date | Stars |
|---|---|
| 2026-07-24 | 2049 |
| 2026-07-25 | 2049 |
| 2026-07-28 | 2052 |
| 2026-07-30 | 2052 |
| 2026-07-31 | 2054 |
| 2026-08-02 | 2054 |
| 2026-08-03 | 2054 |
| 2026-08-06 | 2054 |
Today
— stars today
This week
+2 stars this week
This month
— stars this month
Momentum
17.0
growth rate 0.10%/day
[](https://badge.fury.io/py/vizdoom)
[](https://badge.fury.io/py/vizdoom) [](https://github.com/Farama-Foundation/ViZDoom/actions/workflows/build-and-test.yml) [](https://pre-commit.com/) [](https://github.com/psf/black)
<p align="center">
<img src="https://raw.githubusercontent.com/Farama-Foundation/ViZDoom/main/docs/_static/img/vizdoom-text.png" width="500px"/>
</p>
ViZDoom allows developing AI **bots that play Doom using only visual information** (the screen buffer). It is primarily intended for research in machine visual learning, and deep reinforcement learning, in particular.
ViZDoom is based on [ZDoom](https://zdoom.org) engine to provide the game mechanics.
## Features
- API for Python (including [Gymnasium](https://gymnasium.farama.org/)/Gym wrappers) and C++,
- Multi-platform (Linux, macOS, Windows),
- Fast (up to 7000 frames/steps per second in sync mode, single-threaded on a modern CPU),
- Lightweight (few MBs),
- Easy-to-create custom scenarios (visual editors, powerful scripting language, and examples available),
- Async and sync single-player and multiplayer modes,
- Customizable resolution and rendering parameters,
- Access to the depth buffer (3D vision),
- Automatic labeling and categorization of game objects visible in the frame,
- Access to the audio buffer,
- Access to the list of actors/objects and map geometry,
- Access to in-game text messages and notifications,
- Off-screen rendering,
- Episodes recording,
- In-game time scaling in async mode.
ViZDoom API is **reinforcement learning** friendly (suitable also for learning from demonstration, apprenticeship learning or apprenticeship via inverse reinforcement learning, etc.).
## Cite as
> M Wydmuch, M Kempka & W Jaśkowski, ViZDoom Competitions: Playing Doom from Pixels, IEEE Transactions on Games, vol. 11, no. 3, pp. 248-259, 2019
([arXiv:1809.03470](https://arxiv.org/abs/1809.03470))
```
@article{Wydmuch2019ViZdoom,
author = {Marek Wydmuch and Micha{\l} Kempka and Wojciech Ja\'skowski},
title = {{ViZDoom} {C}ompetitions: {P}laying {D}oom from {P}ixels},
journal = {IEEE Transactions on Games},
year = {2019},
volume = {11},
number = {3},
pages = {248--259},
doi = {10.1109/TG.2018.2877047},
note = {The 2022 IEEE Transactions on Games Outstanding Paper Award}
}
```
or/and
> M. Kempka, M. Wydmuch, G. Runc, J. Toczek & W. Jaśkowski, ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning, IEEE Conference on Computational Intelligence and Games, pp. 341-348, Santorini, Greece, 2016 ([arXiv:1605.02097](http://arxiv.org/abs/1605.02097))
```
@inproceedings{Kempka2016ViZDoom,
author = {Micha{\l} Kempka and Marek Wydmuch and Grzegorz Runc and Jakub Toczek and Wojciech Ja\'skowski},
title = {{ViZDoom}: A {D}oom-based {AI} Research Platform for Visual Reinforcement Learning},
booktitle = {IEEE Conference on Computational Intelligence and Games},
year = {2016},
address = {Santorini, Greece},
month = {Sep},
pages = {341--348},
publisher = {IEEE},
doi = {10.1109/CIG.2016.7860433},
note = {The Best Paper Award}
}
```
## Python quick start
### Linux
To install the latest release of ViZDoom, just run:
```sh
pip install vizdoom
```
Both x86-64 and AArch64 (ARM64) architectures are supported.
Wheels are available for Python 3.9+ on Linux.
⚠️ To use audio features, you need OpenAL install in your system.
On apt-based distros (Ubuntu, DExcerpt of 9,956 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:02ee9d60093a0b7b, topic:reinforcement-learning, topic:gymnasium, desc:reinforcement learning