发表机构
Tianjin University; Shandong Agriculture and Engineering University（天津大学; 山东农业工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多目标无人机边缘计算调度，提出偏好条件决策变换器PrefDT，将离线搜索提炼为运行时控制，一次训练即可输出帕累托前沿任意点，并在仿真中优于26种方法变体。
AI 中文摘要
无人机移动边缘计算（MEC）机队在能量与延迟之间进行权衡，其调度方案构成帕累托前沿；当机队可在运行时被要求达到该前沿上的任意点时，我们称该调度器为可操作的。我们提出PrefDT，据我们所知，这是首个针对联合轨迹、关联与卸载调度问题的偏好条件决策变换器。其思想源于语言建模：我们将期望的权衡作为输入提供给模型，使得单一模型只需离线训练一次，即可在一次 rollout 中返回曲线上任意期望点。机队状态通过带每用户旁路的注意力池化进行汇总，因此当用户报告丢失时调度器仍能继续工作。能量目标是一个运行预算，由机队实际消耗量递减。因此，当风或负载使消耗偏离计划时，策略能够跟踪差异并保持其预算。由于不存在带偏好标签的飞行语料库，我们设计了一个蒸馏流水线并自行构建语料库。在与26种方法变体的仿真对比中，PrefDT在所有学习方法中产生了最佳权衡曲线，并在飞行途中推进成本上升一半时，将其能量预算保持在0.6%以内。
英文摘要
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 the best of our knowledge the first preference-conditioned Decision Transformer for the problem of joint trajectory, association and offloading scheduling. Its idea comes from language modeling: we hand the model the desired trade-off as an input, such that a single model only needs to be trained once offline to return any desired point on the curve in one rollout. The fleet's state is summarized by attention pooling with a per-user bypass, so the scheduler keeps working when user reports are lost. The energy target is a running budget decremented by what the fleet actually spends. As a result, when wind or load pushes consumption off the plan, the policy can track the difference and hold its budget. Because no corpus of preference-labeled flights exists, we design a distillation pipeline and build the corpus by ourselves. In simulation against 26 method variants, PrefDT produces the best trade-off curve of any learned method and holds its energy budget to within 0.6% when propulsion cost rises by half in mid-flight.
CommentsIncludes supplementary material
