Multi-Agent Deep Reinforcement Learning for Task Offloading in UAV-Assisted Mobile Edge Computing

• Published in: IEEE transactions on wireless communications Vol. 21; no. 9; pp. 6949 - 6960
• Main Authors: Zhao, Nan, Ye, Zhiyang, Pei, Yiyang, Liang, Ying-Chang, Niyato, Dusit
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cloud computing; Computation offloading; Computational geometry; Convexity; cooperative offloading; Deep learning; deep reinforcement learning; Edge computing; Machine learning; Manganese; Markov processes; Mobile computing; Mobile edge computing; Multiagent systems; Network latency; Optimization; Power management; Quality of service architectures; Resource management; Servers; Task analysis; task offloading; Trajectory; UAV networks; Unmanned aerial vehicles; Wireless communication; Wireless communications; Wireless networks
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2022.3153316

Mobile edge computing can effectively reduce service latency and improve service quality by offloading computation-intensive tasks to the edges of wireless networks. Due to the characteristic of flexible deployment, wide coverage and reliable wireless communication, unmanned aerial vehicles (UAVs) have been employed as assisted edge clouds (ECs) for large-scale sparely-distributed user equipment. Considering the limited computation and energy capacities of UAVs, a collaborative mobile edge computing system with multiple UAVs and multiple ECs is investigated in this paper. The task offloading issue is addressed to minimize the sum of execution delays and energy consumptions by jointly designing the trajectories, computation task allocation, and communication resource management of UAVs. Moreover, to solve the above non-convex optimization problem, a Markov decision process is formulated for the multi-UAV assisted mobile edge computing system. To obtain the joint strategy of trajectory design, task allocation, and power management, a cooperative multi-agent deep reinforcement learning framework is investigated. Considering the high-dimensional continuous action space, the twin delayed deep deterministic policy gradient algorithm is exploited. The evaluation results demonstrate that our multi-UAV multi-EC task offloading method can achieve better performance compared with the other optimization approaches.