Collaborative Task Offloading Optimization in Multi-UAV-Assisted Mobile Edge Computing
Multi-UAV mobile edge computing can provide flexible computing services for ground users, but it introduces coupled decisions in user association, channel allocation, cooperative offloading, and power control. This paper studies collaborative task offloading in a multi-UAV-assisted MEC system. A multi-stage service process is modeled, including user-to-UAV uploading, local computation, UAV-to-UAV cooperative transmission, cooperative computation, and result return. A comprehensive performance objective is formulated by jointly considering task success rate, delay cost, and energy consumption. To solve the resulting mixed discrete-continuous sequential decision problem, we propose GNN-MAHPPO, a multi-agent hybrid-action PPO algorithm enhanced by dual-layer heterogeneous graph attention. The proposed method jointly optimizes user association, channel allocation, cooperative offloading ratios, and transmit powers. Simulation results show that the proposed method achieves a higher task success rate and lower completion delay and energy consumption than the representative baselines, demonstrating its effectiveness in coordinating access, cooperation, and resource allocation.