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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.
Reaver: Modular Deep Reinforcement Learning Framework. Focused on StarCraft II. Supports Gym, Atari, and MuJoCo.
| Date | Stars |
|---|---|
| 2026-07-24 | 561 |
| 2026-07-25 | 561 |
| 2026-07-28 | 561 |
| 2026-07-30 | 561 |
| 2026-07-31 | 561 |
| 2026-08-06 | 561 |
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# Reaver: Modular Deep Reinforcement Learning Framework
[](https://youtu.be/gEyBzcPU5-w)
[](https://youtu.be/gEyBzcPU5-w)
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**Project status:** No longer maintained!
Unfortunately, I am no longer able to further develop or provide support to the project.
## Introduction
Reaver is a modular deep reinforcement learning framework with a focus on various StarCraft II based tasks, following in DeepMind's footsteps
who are pushing state-of-the-art of the field through the lens of playing a modern video game with human-like interface and limitations.
This includes observing visual features similar (though not identical) to what a human player would perceive and choosing actions from similar pool of options a human player would have.
See [StarCraft II: A New Challenge for Reinforcement Learning](https://arxiv.org/abs/1708.04782) article for more details.
Though development is research-driven, the philosophy behind Reaver API is akin to StarCraft II game itself -
it has something to offer both for novices and experts in the field. For hobbyist programmers Reaver offers all the tools
necessary to train DRL agents by modifying only a small and isolated part of the agent (e.g. hyperparameters).
For veteran researchers Reaver offers simple, but performance-optimized codebase with modular architecture:
agent, model, and environment are decoupled and can be swapped at will.
While the focus of Reaver is on StarCraft II, it also has full support for other popular environments, notably Atari and MuJoCo.
Reaver agent algorithms are validated against reference results, e.g. PPO agent is able to match [
Proximal Policy Optimization Algorithms](https://arxiv.org/abs/1707.06347). Please see [below](#but-wait-theres-more) for more details.
## Installation
### PIP Package
Easiest way to install Reaver is through the `PIP` package manager:
pip install reaver
You can also install additional extras (e.g. `gym` support) through the helper flags:
pip install reaver[gym,atari,mujoco]
### Manual Installation
If you plan to modify `Reaver` codebase you can retain its module functionality by installing from source:
```
$ git clone https://github.com/inoryy/reaver-pysc2
$ pip install -e rExcerpt of 17,647 characters
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Roman Ring · @deepmind
270
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:848a222b2721a34a, topic:deep-learning, topic:tensorflow
matched fp:848a222b2721a34a, topic:reinforcement-learning, desc:reinforcement learning, readme:reinforcement learning