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English(EN) Worst-Case Discovery and Runtime Protection for RL-Based Network Controllers

ReGuard框架识别并保护基于强化学习的网络控制器免受最坏情况的影响

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了ReGuard，一个旨在识别和缓解基于强化学习（RL）的网络控制器最坏情况性能下降的新框架。该框架解决了RL控制器平均表现良好，但在特定网络条件下可能遭受严重性能下降的挑战。ReGuard将场景发现构建为双层遗憾最大化问题，并将发现的风险状态编译成轻量级逻辑规则，以便在运行时进行干预，而无需重新训练RL模型。 AI

影响 该框架可以提高AI驱动的网络管理系统的可靠性和鲁棒性。

排序理由 该集群包含一篇详细介绍基于强化学习的网络控制器新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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ReGuard框架识别并保护基于强化学习的网络控制器免受最坏情况的影响

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该集群包含一篇详细介绍基于强化学习的网络控制器新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Hongyu H\`e, Minhao Jin, Maria Apostolaki ·

    面向基于强化学习的网络控制器最坏情况发现与运行时保护

    arXiv:2605.04373v2 Announce Type: replace-cross Abstract: RL-based controllers achieve strong average-case performance in networking tasks such as congestion control and adaptive bitrate streaming. Yet their performance can degrade severely under network conditions where strong p…

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