arXiv Machine Learning October 3, 2026 By Sabrina Saika, Yinuo Du, Aritran Piplai

Crossing the Cyber Divide: Sim-to-Sim and Sim-to-Real Transfer for RL Agents

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The paper investigates how reinforcement learning agents trained in one cyber simulation can be transferred to other simulators or real environments. It introduces a framework that decouples state alignment from action translation, allowing zero‑shot policy transfer without retraining. Experiments across four cyber platforms show that transferred policies can preserve performance in closely aligned settings and achieve substantial win rates in more divergent environments.

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