---
license: openrail
language:
- en
size_categories:
- 100M<n<1B
---

102,400,000 timesteps, Multi-Agent Reinforcement Learning

Total Environment Steps= 10 parallel environments × 7,000 episodes ×2,048 steps= 102400000 Training Timesteps

-The Red Agent’s goal is to discover vulnerabilities, elevate privileges, compromise assets, and maintain persistence. Its action space can be modeled after phases of the
MITRE ATT&CK framework.

-The Blue Agent’s goal is to maintain system availability, reduce the attack surface, detect malicious behavior, deceive the attacker with honeypots and evict the intruder. Its action space
consists of defensive configurations and mitigation steps.

Pure Zero-Sum Opposition: Red's gain is Blue's absolute loss and vice versa

Strictly for educational purposes. Do not modify to use against unauthorized Networks

Defense Agent in Action: https://www.youtube.com/watch?v=3g3plOjZkJw