北京信息科技大学学报（自然科学版）2023，Vol.38Issue(6)：1-10,10.DOI:12.16508/j.cnki.11-5866/n.2023.06.001
基于联邦深度强化学习的多无人机轨迹规划算法
A multi-UAVs trajectory planning algorithm based on federated deep reinforcement learning
摘要
Abstract
For the multi-UAVs cooperative service ground user mobile edge computing service,a model was constructed with the objectives of multi-UAVs service ground user fairness and computation delay weighted sum maximization,and the scheduling of UAV trajectories and task offloading ratios were jointly optimized.A multi-UAVs trajectory planning algorithm based on federated deep reinforcement learning in mobile edge computing scenarios was proposed.The algorithm first deployed independent deep reinforcement learning models on each UAV,so that each UAV learned to acquire a locally optimal model based on its own acquired information.Secondly,a federated learning framework was introduced to enable multi-UAVs to collaboratively serve ground users through information aggregation,so that the service effect reached the global optimum.Simulation results show that the proposed scheme effectively improves the fairness and delay compared with the multi-intelligence deep reinforcement learning without information interaction.关键词
无人机通信/移动边缘计算/深度强化学习/联邦学习/轨迹规划/公平性Key words
UAV communication/mobile edge computing/deep reinforcement learning/federated learning/trajectory planning/fairness分类
信息技术与安全科学引用本文复制引用
王鉴威,李学华,陈硕..基于联邦深度强化学习的多无人机轨迹规划算法[J].北京信息科技大学学报（自然科学版）,2023,38(6):1-10,10.基金项目
国家自然科学基金项目(61901043) （61901043）
北京信息科技大学"勤信人才"培育计划(QXTCPB202101) （QXTCPB202101）
北京市教委科研计划科技一般项目(KM202211232010) （KM202211232010）
