English(EN) Model-Free Reinforcement Learning Control for Resilient Cyber-Physical Systems

无模型强化学习控制器增强网络物理系统抵御攻击的能力 · arXiv论文

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-17 13:43

一篇新发表在arXiv上的研究论文探讨了无模型强化学习（RL）控制器在增强网络物理系统抵御网络攻击韧性方面的有效性。该研究分析了四种RL奖励类型，发现Lyapunov奖励在最小化跟踪误差的同时提供了卓越的韧性。近端策略优化（PPO）的表现优于深度确定性策略梯度（DDPG），显著降低了关键绩效指标的方差。 AI

影响这项研究可能带来更强大的AI驱动型安全措施，用于关键基础设施和工业控制系统。

排序理由该集群包含一篇在arXiv上发表的研究论文，详细介绍了网络物理系统强化学习的新发现。

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报道来源 [2]

arXiv cs.LG TIER_1 English(EN) · Hugo O. Garc\'es, Alejandro J. Rojas, Bernardo A. Hern\'andez, Andr\'es Escalona, Jonathan M. Palma, Md. Rezwan Parvez, Bhushan Gopaluni, Sirish L. Shah · 2026-06-18 04:00

Model-Free Reinforcement Learning Control for Resilient Cyber-Physical Systems

arXiv:2606.19069v1 Announce Type: cross Abstract: This paper compares the performance of model-free controllers on a nonlinear system under cyberattacks, including false data injection and denial-of-service attacks. Four RL reward types are analyzed for accuracy, cost, and resili…
arXiv cs.LG TIER_1 English(EN) · Sirish L. Shah · 2026-06-17 13:43

Model-Free Reinforcement Learning Control for Resilient Cyber-Physical Systems

This paper compares the performance of model-free controllers on a nonlinear system under cyberattacks, including false data injection and denial-of-service attacks. Four RL reward types are analyzed for accuracy, cost, and resilience. Results show that the Lyapunov reward offers…

报道来源 [2]

Model-Free Reinforcement Learning Control for Resilient Cyber-Physical Systems

Model-Free Reinforcement Learning Control for Resilient Cyber-Physical Systems

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