English(EN) Crossing the Cyber Divide: Sim-to-Sim and Sim-to-Real Transfer for RL Agents

新框架使强化学习智能体能够跨网络模拟器进行迁移

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-03 04:00

研究人员开发了一个新框架，以提高强化学习智能体在不同网络安全模拟环境中的迁移能力。所提出的方法将状态对齐与动作翻译分开，使得在一种模拟器中训练的策略能够在另一种模拟器中运行，而无需重新训练。实验表明，该方法能够实现零样本迁移，在高度匹配的环境中保持显著的性能，并在从60.5%的源性能迁移策略时达到45.2%的胜率。该框架在模拟虚拟机环境中也表现出与原生策略强大的行为相似性，Jensen-Shannon散度为0.085。 AI

影响通过实现更强大和更具泛化性的威胁检测和响应能力，增强了强化学习智能体在网络安全领域的实际应用。

排序理由学术论文，详细介绍了强化学习智能体在网络安全模拟中的迁移能力的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Sabrina Saika, Yinuo Du, Aritran Piplai · 2026-10-03 04:00

跨越网络鸿沟：强化学习智能体的Sim-to-Sim与Sim-to-Real迁移

arXiv:2610.00759v1 Announce Type: cross Abstract: Cyber attack agents are typically trained and evaluated within a single simulator, making it unclear whether learned policies transfer beyond the environments in which they were developed. This limitation hinders both deployment a…

本文如何被排名

报道来源 [1]

跨越网络鸿沟：强化学习智能体的Sim-to-Sim与Sim-to-Real迁移

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