New framework enables RL agents to transfer across cyber simulators

Researchers have developed a new framework to improve the transferability of reinforcement learning agents across different cybersecurity simulation environments. The proposed method separates state alignment from action translation, allowing policies trained in one simulator to function in another without requiring retraining. Experiments demonstrated that this approach enables zero-shot transfer, maintaining significant performance in closely aligned environments and achieving a 45.2% win rate when transferring policies from a source performance of 60.5%. The framework also showed strong behavioral similarity to native policies in emulated virtual machine environments, with a Jensen-Shannon divergence of 0.085. AI

IMPACT Enhances the practical application of RL agents in cybersecurity by enabling more robust and generalizable threat detection and response capabilities.

RANK_REASON Academic paper detailing a new framework for RL agent transferability in cybersecurity simulations. [lever_c_demoted from research: ic=1 ai=1.0]

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