Trajectory design for multi-UAV-assisted mobile edge computing based on multi-agent deep reinforcement learning
XU Shaoyi
YANG Lei
Abstract：Unmanned Aerial Vehicle(UAV)-assisted Mobile Edge Computing(MEC)networks can provide high-quality computational services to ground User Equipment(UE),but real-time trajectory design for multiple UAVs remains a significant challenge.To address this issue,a trajectory design al-gorithm based on multi-agent deep reinforcement learning is proposed,utilizing the Multi-Agent Deep Deterministic Policy Gradient(MADDPG)framework to collaboratively design UAV trajectories.Considering the limited battery capacity of UAVs,a critical constraint on UAV network performance,the optimization problem is formulated to improve the sum of UAV energy efficiencies.This involves jointly optimizing the trajectories of UAV clusters and the offloading decisions of UEs.Each agent in-teracts with the edge computing network environment,observes its local state,and determines trajec-tory coordinates via an Actor network.The Critic network is trained by incorporating the action and ob-servation of other agents,thereby refining the trajectory policy generated by the Actor network.Simu-lation results demonstrate that the MADDPG-based trajectory design algorithm exhibits excellent con-vergence and robustness,significantly enhancing UAV energy efficiency.Specifically,the proposed al-gorithm outperforms the random flight algorithm by 120%at most,the circular flight algorithm by 20%at most,and the Deep Deterministic Policy Gradient(DDPG)algorithm by 5%to 10%.
Keywords：UAV trajectory designMobile Edge Computing(MEC)reinforcement learningMulti-Agent Deep Deterministic Policy Gradient(MADDPG)
Publication Date：2024-10-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 1-9 )