计算机科学
强化学习
任务(项目管理)
分布式计算
移动边缘计算
移动设备
高效能源利用
GSM演进的增强数据速率
边缘计算
计算机网络
人工智能
管理
电气工程
经济
工程类
操作系统
作者
Pengfei Wang,Hao Yang,Guangjie Han,Ruiyun Yu,Leyou Yang,Geng Sun,Heng Qi,Xiaopeng Wei,Qiang Zhang
标识
DOI:10.1109/tmc.2024.3439696
摘要
Unmanned Aerial Vehicle (UAV)-enabled mobile edge computing has been proposed as an efficient task-offloading solution for user equipments (UEs). Nevertheless, the presence of heterogeneous UAVs makes centralized navigation policies impractical. Decentralized navigation policies also face significant challenges in knowledge sharing among heterogeneous UAVs. To address this, we present the soft hierarchical deep reinforcement learning network (SHDRLN) and dual-end federated reinforcement learning (DFRL) as a decentralized navigation policy solution. It enhances overall task-offloading energy efficiency for UAVs while facilitating knowledge sharing. Specifically, SHDRLN, a hierarchical DRL network based on maximum entropy learning, reduces policy differences among UAVs by abstracting atomic actions into generic skills. Simultaneously, it maximizes the average efficiency of all UAVs, optimizing coverage for UEs and minimizing task-offloading waiting time. DFRL, a federated learning (FL) algorithm, aggregates policy knowledge at the cloud server and filters it at the UAV end, enabling adaptive learning of navigation policy knowledge suitable for the UAV's performance parameters. Extensive simulations demonstrate that the proposed solution not only outperforms other baseline algorithms in overall energy efficiency but also achieves more stable navigation policy learning under different levels of heterogeneity of different UAV performance parameters.
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