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

The paper introduces PrefDT, a preference-conditioned Decision Transformer designed for multi‑objective scheduling of UAV mobile edge computing fleets. PrefDT accepts a desired energy‑delay trade‑off as input, enabling a single offline‑trained model to generate any point on the Pareto front during runtime. The authors employ attention pooling with a per‑user bypass to maintain scheduler operation when user reports are lost, and a distillation pipeline to create a preference‑labeled flight corpus, achieving superior trade‑off curves and tight energy budget adherence in simulations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

• llms
• efficiency