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A curated list of resources dedicated to reinforcement learning applied to cyber security.
| Date | Stars |
|---|---|
| 2026-07-31 | 1076 |
| 2026-08-02 | 1077 |
| 2026-08-04 | 1076 |
| 2026-08-06 | 1077 |
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<h1 align="center"> Awesome Reinforcement Learning <br>for Cyber Security </h1>
<p align="center">
<img src="https://awesome.re/badge.svg">
<a href="https://github.com/Limmen/awesome-rl-for-cybersecurity">
<img src="https://img.shields.io/badge/Awesome-AwesomeRLForCyber-orange">
</a>
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A curated list of resources dedicated to reinforcement learning applied to cyber security.
Note that the list includes only work that uses reinforcement learning, general machine learning methods applied to cyber security are not included in this list.
For other related curated lists, see :
* [Awesome Machine Learning for Cyber Security](https://github.com/jivoi/awesome-ml-for-cybersecurity)
* [Awesome Adversarial Machine Learning](https://github.com/yenchenlin/awesome-adversarial-machine-learning)
<p align="center">
<img src="imgs/network_chess.png" width="50%", height="50%">
</p>
## Table of Contents
- [RL-Environments](#-environments)
- [Papers](#-papers)
- [Books](#-books)
- [Blogposts](#-blogposts)
- [Talks](#-talks)
- [Miscellaneous](#-miscellaneous)
## [↑](#table-of-contents) Environments
### `CSLE: The Cyber Security Learning Environment`
<table>
<tbody>
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<td width='50%' align='center'>
<img src='imgs/csle_logo_cropped.png' />
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<a href='https://github.com/Limmen/csle'>CSLE: The Cyber Security Learning Environment</a>
<ul>
<li>
CSLE is a platform for evaluating and developing reinforcement learning agents for control problems in cyber security. It can be considered as a cyber range specifically designed for reinforcement learning agents. Everything from network emulation, to simulation and implementation of network commands have been co-designed to provide an environment where it is possible to train and evaluate reinforcement learning agents on practical problems in cyber security.
</li>
<li>
Paper: <a href="https://arxiv.org/abs/2604.15590">(2026) CSLE: A Reinforcement Learning Platform for Autonomous Security Management</a><br/>
Thesis: <a href="https://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A1912164&dswid=7946">(2024) Optimal Security Response to Network Intrusions in IT Systems</a><br/>
</li>
</ul>
</td>
</tr>
</tbody>
</table>
### `Continuous CyberBattleSim`
<table>
<tbody>
<tr>
<td width='50%' align='center'>
<a href="https://github.com/terranovafr/C-CyberBattleSim"><img src='imgs/c-cyberbattlesim.png' width=370 /></a>
</td>
<td width='50%'>
<a href=https://github.com/terranovafr/C-CyberBattleSim>C-CyberBattleSim</a>
<ul>
<li>
An enhanced version of Microsoft CyberBattleSim that integrates graph neural networks and language models to create generalizable, scalable continuous spaces for reinforcement learning, features an extended scenario generation pipeline utilizing Shodan and the NVD, and provides a unified framework for RL training and evaluation.<br><br>
Paper: <a href="https://ieeexplore.ieee.org/document/11352493">(2025) Scalable and Generalizable RL Agents for Attack Path Discovery via Continuous Invariant Spaces</a></br>
Documentation: <a href="https://c-cyberbattlesim.readthedocs.io/en/latest/home.html">Read The DoExcerpt of 129,500 characters
Read on GitHub
Kim Hammar · Imperial College · United Kingdom
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Vasilios Mavroudis
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f77274ae4401573e, desc:reinforcement learning