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English(EN) The Operable Pareto Front: Distilling Offline Search into Run-Time Control for Multi-Objective UAV Edge-Computing Scheduling

新的决策 Transformer 优化无人机机队调度以平衡能源和延迟

研究人员开发了 PrefDT,这是一种新颖的、受偏好条件约束的决策 Transformer,专为移动边缘计算 (MEC) 环境中运行的无人机 (UAV) 机队的多目标调度而设计。该系统旨在优化能源消耗和延迟之间的权衡,允许单个训练模型根据期望的性能点在运行时动态调整调度。PrefDT 利用注意力池化进行状态摘要,并包含一个蒸馏管道来生成训练数据,在模拟中表现优于其他 26 种方法,即使在飞行中推进成本增加的情况下,也能将能源预算保持在 0.6% 以内。 AI

影响 为边缘计算系统引入了一种新颖的实时多目标优化方法,有望提高自主系统的效率。

排序理由 该条目是一篇学术论文,详细介绍了一种解决特定技术问题的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的决策 Transformer 优化无人机机队调度以平衡能源和延迟

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该条目是一篇学术论文,详细介绍了一种解决特定技术问题的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Qiao Liao, Zhiyong Feng, Bin Wu, Guodong Fan ·

    可操作的帕累托前沿:将离线搜索蒸馏为多目标无人机边缘计算调度的运行时间控制

    arXiv:2609.17992v1 Announce Type: cross Abstract: A UAV mobile edge computing (MEC) fleet trades energy against delay, and its schedules form a Pareto front; we call a scheduler operable when the fleet can be asked for any point on that front at run time. We propose PrefDT, to th…