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

arXiv:2609.17992 · cs.NI, cs.LG · Submitted 2026-09-16 · Read on arXiv

cs.NI, cs.LG

Submitted: 2026-09-16

Updated: 2026-09-16

Comments: Includes supplementary material

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

Transformer — 20× in this paper · explained in 2 episodes
Fidelity — 13× in this paper · explained in 2 episodes
Distillation — 11× in this paper · explained in 2 episodes
Reinforcement Learning — 11× in this paper · explained in 4 episodes
Frozen model — 10× in this paper · explained in 3 episodes
Core — 9× in this paper · explained in 2 episodes
Ablation — 6× in this paper · explained in 1 episode
Mobile Edge Computing — 5× in this paper · explained in 2 episodes
Adaptation — 2× in this paper · explained in 1 episode
Calibration — 2× in this paper · explained in 7 episodes
Trace — 2× in this paper · explained in 2 episodes
Association for Computing Machinery — 1× in this paper
Benchmark — 1× in this paper · explained in 2 episodes
Decode — 1× in this paper · explained in 2 episodes
Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
Generalization — 1× in this paper · explained in 13 episodes
Loss Function — 1× in this paper · explained in 2 episodes
Multi-objective Optimization — 1× in this paper · explained in 4 episodes
Online Adaptation — 1× in this paper · explained in 3 episodes
Provenance — 1× in this paper · explained in 4 episodes
Q-learning — 1× in this paper · explained in 3 episodes

Sources

Proximal Policy Optimization Algorithms

Related papers