Cygil: A Cyber Gym For Training Autonomous Agents Over Emulated Network Systems

Abstract

Given the success of reinforcement learning (RL) in various domains, it is promising to explore the application of its methods to the development of intelligent and autonomous cyber agents. Enabling this development requires a representative RL training environment. To that end, this work presents CyGIL: an experimental testbed of an emulated RL training environment for network cyber operations. CyGIL uses a stateless environment architecture and incorporates the MITRE ATT&CK framework to establish a high fidelity training environment, while presenting a sufficiently abstracted interface to enable RL training. Its comprehensive action space and flexible game design allow the agent training to focus on particular advanced persistent threat (APT) profiles, and to incorporate a broad range of potential threats and vulnerabilities. By striking a balance between fidelity and simplicity, it aims to leverage state of the art RL algorithms for application to real-world cyber defence.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyli2021cygil

Related papers

• Beyond Rewards In Reinforcement Learning For Cyber Defence (2026)0.00
• Learning To Communicate In Multi-agent Reinforcement Learning For Autonomous Cyber Defence (2025)0.00
• Deep Reinforcement Learning For Autonomous Cyber Defence: A Survey (2023)0.00
• Learning To Cope With Adversarial Attacks (2019)0.00
• Computerrl: Scaling End-to-end Online Reinforcement Learning For Computer Use Agents (2025)0.00
• The AI Arena: A Framework For Distributed Multi-agent Reinforcement Learning (2021)0.00
• Unveiling The Black Box: A Multi-layer Framework For Explaining Reinforcement Learning-based Cyber Agents (2025)0.00
• Lexci: A Framework For Reinforcement Learning With Embedded Systems (2023)6.34