# CybORG++: An Enhanced Gym for the Development of Autonomous Cyber Agents

arXiv: https://arxiv.org/abs/2410.16324 · HTML: https://mlsanity.com/paper/2410.16324/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 8 of its 11 references that are in the map.
- First posted on arXiv 18 Oct 2024.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 8 of 8)

- [CybORG: A Gym for the Development of Autonomous Cyber Agents](https://mlsanity.com/paper/2108.09118/) (2021)
- [On Autonomous Agents in a Cyber Defence Environment](https://mlsanity.com/paper/2309.07388/) (2023)
- [Automated Cyber Defence: A Review](https://mlsanity.com/paper/2303.04926/) (2023)
- [Reward Shaping for Happier Autonomous Cyber Security Agents](https://mlsanity.com/paper/2310.13565/) (2023)
- [Inroads into Autonomous Network Defence using Explained Reinforcement Learning](https://mlsanity.com/paper/2306.09318/) (2023)
- [Autonomous Network Defence using Reinforcement Learning](https://mlsanity.com/paper/2409.18197/) (2024)
- [Optimal Defender Strategies for CAGE-2 using Causal Modeling and Tree Search](https://mlsanity.com/paper/2407.11070/) (2024)
- [Canaries and Whistles: Resilient Drone Communication Networks with (or without) Deep Reinforcement Learning](https://mlsanity.com/paper/2312.04940/) (2023)

## Built on by (most cited first, 11 of 11)

- [SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecurity](https://mlsanity.com/paper/2602.08690/) (2026)
- [Beyond Rewards in Reinforcement Learning for Cyber Defence](https://mlsanity.com/paper/2602.04809/) (2026)
- [Building Better Environments for Autonomous Cyber Defence](https://mlsanity.com/paper/2604.08805/) (2026)
- [Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications](https://mlsanity.com/paper/2505.19837/) (2025)
- [Reinforcement Learning for Automated Cybersecurity Penetration Testing](https://mlsanity.com/paper/2507.02969/) (2025)
- [SENTINEL-RL: Offloading Topological Reasoning from LLM Agents in the Security Operations Center](https://mlsanity.com/paper/2609.04159/) (2026)
- [Inherently Interpretable and Uncertainty-Aware Models for Online Learning in Cyber-Security Problems](https://mlsanity.com/paper/2411.09393/) (2024)
- [NASimJax: A GPU-Accelerated Policy Learning Framework for Penetration Testing](https://mlsanity.com/paper/2603.19864/) (2026)
- [COHORT: Collaborative Orchestration for Hardening via Offensive Replay on Emulated Topologies](https://mlsanity.com/paper/2606.30479/) (2026)
- [A Cyber Range Evaluation of Autonomous Network Incident Response Agents](https://mlsanity.com/paper/2609.16541/) (2026)
- [Efficient Nash Equilibrium Computation for Cybersecurity Games](https://mlsanity.com/paper/2609.19399/) (2026)

## Abstract (arXiv)

CybORG++ is an advanced toolkit for reinforcement learning research focused on network defence. Building on the CAGE 2 CybORG environment, it introduces key improvements, including enhanced debugging capabilities, refined agent implementation support, and a streamlined environment that enables faster training and easier customisation. Along with addressing several software bugs from its predecessor, CybORG++ introduces MiniCAGE, a lightweight version of CAGE 2, which improves performance dramatically, up to 1000x faster execution in parallel iterations, without sacrificing accuracy or core functionality. CybORG++ serves as a robust platform for developing and evaluating defensive agents, making it a valuable resource for advancing enterprise network defence research.