Deadline-Paced Patrol with Goal-Conditioned Multi-Agent Reinforcement Learning for Sensing-Limited UAV-Assisted Mobile Edge Computing

Abstract

Computing services can be extended to infrastructure-limited areas through unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC). However, service provision continues to be challenging when spatially clustered ground users (GUs) are initially unknown and their intermittent workloads expire within finite validity windows. Therefore, conventional trajectory controllers assuming known GU locations and requests are unsuitable under sensing-limited conditions, where out-of-range workloads remain hidden between visits. Partially observable control has been adopted to address these challenges; however, existing frameworks are insufficient, as the discovery of unknown service regions and the repeated revisits required to re-observe hidden demand are not jointly considered. In this paper, a deadline-paced patrol with goal-conditioned multi-agent reinforcement learning (DPP-GCMARL) framework is proposed. In the proposed framework, a rule-based deadline-paced patrol (DPP) layer converts sensing-derived cell memory into deconflicted target cells using staleness normalized by the collection deadline. The assigned targets are then mapped to continuous movement commands by a learned goal-conditioned movement (GCM) layer. Simulation results show that DPP-GCMARL improves the end-to-end completed-workload ratio compared with multi-agent proximal policy optimization (MAPPO). It also increases exploration-coupled fairness and the worst-cluster collection ratio while reducing the mean travel per UAV. These results substantiate the proposed framework’s potential for deadline-constrained MEC service in infrastructure-limited environments.