Multi-Agent Deep Reinforcement Learning-Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing

• Published in: IEEE transactions on cognitive communications and networking Vol. 7; no. 1; pp. 73 - 84
• Main Authors: Wang, Liang, Wang, Kezhi, Pan, Cunhua, Xu, Wei, Aslam, Nauman, Hanzo, Lajos
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.03.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Control algorithms; Control theory; Deep learning; Edge computing; Energy consumption; Indexes; Machine learning; MADDPG; Mobile computing; mobile edge computing; Multi-agent deep reinforcement learning; Multiagent systems; Optimization; Task analysis; Trajectory; Trajectory control; Trajectory planning; UAV; Unmanned aerial vehicles
• ISSN: 2332-7731; 2332-7731
• DOI: 10.1109/TCCN.2020.3027695

An unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) framework is proposed, where several UAVs having different trajectories fly over the target area and support the user equipments (UEs) on the ground. We aim to jointly optimize the geographical fairness among all the UEs, the fairness of each UAV' UE-load and the overall energy consumption of UEs. The above optimization problem includes both integer and continues variables and it is challenging to solve. To address the above problem, a multi-agent deep reinforcement learning based trajectory control algorithm is proposed for managing the trajectory of each UAV independently, where the popular Multi-Agent Deep Deterministic Policy Gradient (MADDPG) method is applied. Given the UAVs' trajectories, a low-complexity approach is introduced for optimizing the offloading decisions of UEs. We show that our proposed solution has considerable performance over other traditional algorithms, both in terms of the fairness for serving UEs, fairness of UE-load at each UAV and energy consumption for all the UEs.