Top AI Repos — open-source AI, indexed and scored
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.
High throughput synchronous and asynchronous reinforcement learning
| Date | Stars |
|---|---|
| 2026-07-24 | 1008 |
| 2026-07-25 | 1009 |
| 2026-07-28 | 1009 |
| 2026-07-30 | 1009 |
| 2026-07-31 | 1012 |
| 2026-08-01 | 1013 |
| 2026-08-05 | 1013 |
| 2026-08-06 | 1013 |
Today
— stars today
This week
+4 stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.40%/day
[](https://github.com/alex-petrenko/sample-factory/actions/workflows/test-ci.yml) [](https://codecov.io/gh/alex-petrenko/sample-factory) [](https://github.com/alex-petrenko/sample-factory/actions/workflows/pre-commit.yml) [](https://samplefactory.dev) [](https://github.com/psf/black) [](https://pycqa.github.io/isort/) [](https://github.com/alex-petrenko/sample-factory/blob/master/LICENSE) [](https://pepy.tech/project/sample-factory) [<img src="https://img.shields.io/discord/987232982798598164?label=discord">](https://discord.gg/BCfHWaSMkr) <!-- [](https://results.pre-commit.ci/latest/github/wmFrank/sample-factory/master)--> <!-- [](https://wakatime.com/badge/github/alex-petrenko/sample-factory)--> # Sample Factory High-throughput reinforcement learning codebase. Version **2** is out! 🤗 **Resources:** * **Documentation:** [https://samplefactory.dev](https://samplefactory.dev) * **Paper:** https://arxiv.org/abs/2006.11751 * **Citation:** [BibTeX](https://github.com/alex-petrenko/sample-factory#citation) * **Discord:** [https://discord.gg/BCfHWaSMkr](https://discord.gg/BCfHWaSMkr) * **Twitter (for updates):** [@petrenko_ai](https://twitter.com/petrenko_ai) * **Talk (circa 2021):** https://youtu.be/lLG17LKKSZc ### What is Sample Factory? Sample Factory is one of the fastest RL libraries focused on very efficient synchronous and asynchronous implementations of policy gradients (PPO). Sample Factory is thoroughly tested and used by many researchers and practitioners. Our implementation is known to reach state-of-the-art (SOTA) performance across a wide range of domains, while minimizing the required training time and hardware requirements. Clips below demonstrate ViZDoom, IsaacGym, DMLab-30, Megaverse, Mujoco, and Atari agents trained with Sample Factory: <p align="middle"> <img src="https://github.com/alex-petrenko/sf_assets/blob/main/gifs/vizdoom.gif?raw=true" width="360" alt="VizDoom agents traned using Sample Factory 2.0"> <img src="https://github.com/alex-petrenko/sf_assets/blob/main/gifs/isaac.gif?raw=true" width="360" alt="IsaacGym agents traned using Sample Factory 2.0"> <br/> <img src="https://github.com/alex-petrenko/sf_assets/blob/main/gifs/dmlab.gif?raw=true" width="380" alt="DMLab-30 agents traned using Sample Factory 2.0"> <img src="https://github.com/alex-petrenko/sf_assets/blob/main/gifs/megaverse.gif?raw=true" width="340" alt="Megaverse agents traned using Sample Factory 2.0"> <br/> <img src="https://github.com/alex-petrenko/sf_assets/blob/main/gifs/mujoco.gif?raw=true" width="390" alt="Mujoco agents traned using Sample Factory 2.0"> <img src="https://github.com/alex-petrenko/sf_assets/blob/main/gifs/atari.gif?raw=true" width="330" alt="Atari agents traned using Sample Factory 2.0"> </p> **Key features:** * Highly optimized algorithm [architecture](https://www.samplefactory.dev/06-architecture/overview/) for maximum learning throughput * [Synchronous and asynchronous](https://www.samplefactory.dev/07-advanced-topics/sync-async/) training regimes * [Serial (single-process) mode](https://www.samplefacto
Excerpt of 11,514 characters
Read on GitHub
40
36
33
25
22
Edward Beeching · Hugging Face · France
21
20
17
Erik Wijmans
9
5
Gautam Salhotra
4
4
2
2
2
2
2
1
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f671c47fab1a0787, topic:reinforcement-learning, desc:reinforcement learning, readme:reinforcement learning