郑州大学学报（工学版）2025，Vol.46Issue(4)：16-23,31,9.DOI:10.13705/j.issn.1671-6833.2025.01.018
基于深度强化学习的无人机边缘计算任务卸载策略
Task Offloading Strategy of UAV Edge Computing Based on Deep Reinforcement Learning
摘要
Abstract
Aiming at the problems such as lack of infrastructure,high task delay and high bandwidth demand in complex geographical conditions,a multi-stage mobile edge computing system model which combined computing off-loading and power distribution was proposed.In this model,a server equipped with MEC was deployed near the UAV to provide computing services,and the problems such as task offloading,power consumption and computing resource allocation of the UAV were comprehensively analyzed and the measurement methods were given.At the same time,the types of tasks that the UAV could perform and the requirements of the CPU and GPU on the UAV were considered.The problem was expressed as a mixed integer nonlinear problem.A task computing offloading al-gorithm based on deep reinforcement learning was proposed to solve this problem.Based on the improved double deep Q learning algorithm,the algorithm used deep neural network to find the mapping between UAVs in deep rein-forcement learning,finding potential patterns from the state space and estimating the optimal action,and used mod-el-free DRL method to enable each UAV to make quick offloading decisions based on local observations.Simulation results showed that the proposed algorithm reduced the average offloading cost by 42.8%compared with LCGP algo-rithm.Compared with DDPG algorithm,the energy consumption was reduced by 16%.Compared with DDQN algo-rithm,the task execution delay was reduced by 12.9%.关键词
无人机/边缘计算/任务卸载/深度强化学习/资源分配Key words
UAV/edge computing/task offloading/deep reinforcement learning/resource allocation分类
信息技术与安全科学引用本文复制引用
王峰,马星宇,孟鹏帅,赵薇,翟伟光..基于深度强化学习的无人机边缘计算任务卸载策略[J].郑州大学学报（工学版）,2025,46(4):16-23,31,9.基金项目
山西省重点研发计划(202102150101008) （202102150101008）
山西省留学人员科技活动项目择优资助项目(20230063) （20230063）
