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AIModelKit > Comparisons > NetForge RL: An Advanced Multi-Agent Cyber Defense Simulation Environment Featuring Durative Actions
Comparisons

NetForge RL: An Advanced Multi-Agent Cyber Defense Simulation Environment Featuring Durative Actions

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Last updated: July 16, 2026 3:00 am
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NetForge RL: An Advanced Multi-Agent Cyber Defense Simulation Environment Featuring Durative Actions
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NetForge RL: A Cutting-Edge Simulation Environment for Cyber Defense

In an era where cyber threats loom large, the need for advanced training environments in cybersecurity has never been more critical. Introducing NetForge RL, a sophisticated multi-agent simulation setting designed specifically for cyber defense efforts. Developed by Igor Jankowski, this innovative platform provides a lifelike experience, enabling security teams to prepare for real-world scenarios through advanced reinforcement learning techniques.

Contents
  • The Need for Realistic Training Environments
  • Key Features of NetForge RL
    • Procedurally Generated Networks
    • Integration with MITRE ATT&CK Framework
    • High-Performance Backend
  • Scenarios and Difficulty Levels
  • Comprehensive Evaluation Tools
  • Reproducibility and Ethical Considerations
  • Submission History and Accessibility
    • Why NetForge RL Matters

The Need for Realistic Training Environments

Training reinforcement-learning agents demands not just theoretical frameworks but environments that closely resemble actual operational settings. Cyber defense requires agents to operate under realistic conditions characterized by:

  • Noisy Data: Cyber scenarios often involve incomplete information, making it challenging to make informed decisions.
  • Partial Observations: Agents must learn to act with limited visibility, mimicking real-world limitations.
  • Coordinated Defense: Multiple defenders work together across a network to thwart evolving threats.
  • Adaptive Adversaries: Simulated attackers must be able to adapt and change their tactics in response to defensive actions.

NetForge RL effectively encapsulates these complexities, making it an invaluable tool for enhancing cybersecurity training.

Key Features of NetForge RL

One of the standout features of NetForge RL is its dual-agent system. This includes a red agent that compromises hosts using limited information, and three zone-split blue agents that respond to threats based on synthesized Security Information and Event Management (SIEM) telemetry.

Procedurally Generated Networks

NetForge RL utilizes procedurally generated enterprise and operational-technology (OT) networks, introducing a variety of scenarios that keep training sessions fresh and challenging. This dynamic aspect ensures that agents are trained across diverse environments, preparing them for varying types of threats.

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Integration with MITRE ATT&CK Framework

The platform employs actions that are structured around the MITRE ATT&CK framework, a globally recognized matrix of tactics and techniques used by cyber adversaries. This alignment enables teams to prepare for real-world attacks in a manner that is both structured and comprehensive.

High-Performance Backend

Performance metrics reveal NetForge RL’s capability for effective learning. Using a JAX backend, it achieves an impressive 2.5 x 10^5 environment-steps/s at batch 4096 on CPU, optimizing training loop iterations. This high-speed ability significantly accelerates the training process, allowing for quicker adjustments and improvements.

Scenarios and Difficulty Levels

NetForge RL ships with five predefined scenarios that come equipped with named difficulty presets. This feature enables users to select environments tailored to their current training needs. The inclusion of a held-out evaluation split allows for more rigorous assessments of agents’ performance and adaptability.

Comprehensive Evaluation Tools

NetForge RL doesn’t just offer a simulation environment; it’s also equipped with a set of six diagnostic probes. Each probe measures a specific defensive skill, offering deep insights into agent performance. Coupled with an evaluation runner that reports 95% confidence intervals, this structure fosters an environment of continuous improvement.

Reproducibility and Ethical Considerations

A strong commitment to reproducibility is ingrained in the design of NetForge RL. The development process details the necessary engineering behind creating the environment, ensuring other researchers can replicate findings effectively. Additionally, a responsible-use statement underscores the importance of ethical considerations in deploying AI for cybersecurity applications.

Submission History and Accessibility

The platform has undergone several iterations to refine its functionality:

  • Version 1: Submitted on April 10, 2026
  • Version 2: Revised on July 14, 2026
  • Version 3: Last revised on July 15, 2026

For those interested in exploring this environment further, a PDF of the paper titled NetForge RL: A Multi-Agent Simulation Environment for Cyber Defense with Durative Actions is available for download, providing in-depth insights and technical details.

Why NetForge RL Matters

As cyber threats evolve, so must our strategies in training cyber defenders. The innovative framework of NetForge RL not only equips agents with practical skills but also empowers teams to face an ever-changing digital landscape. In a field where preparation is key, NetForge RL stands as a beacon of progress, offering a platform that is as adaptable as the threats it is designed to counter.

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