福建师范大学学报（自然科学版）2026，Vol.42Issue(2)：22-32,11.DOI:10.12046/j.issn.1000-5277.2024100018
空天辅助车载边缘计算中基于深度强化学习的计算卸载策略
Deep Reinforcement Learning-Based Computation Offloading Strategy for Aerospace-Assisted Vehicular Edge Computing
摘要
Abstract
To address the computation offloading problem in aerospace-assisted vehicular edge computing scenarios,a deep reinforcement learning-based computation offloading strategy is proposed to support the computation offloading of the Internet of Vehicles in remote areas,considering the het-erogeneity of resources in the space-air-ground integrated network SAGIN)and the task dependency structure in intelligent vehicular applications.The strategy first constructs a network scenario for aero-space-assisted vehicular computing based on the characteristics of different devices,such as on-board terminals,unmanned aerial vehicles,and low Earth orbit satellites.Then,this computation offloading problem is formulated as a Markov decision process MDP).Finally,with the objective of minimizing the average delay and energy consumption during the offloading process,a computation offloading strategy is designed based on an improved deep deterministic policy gradient DDPG)algorithm.Ex-perimental results show that compared with the conventional reinforcement learning algorithms DDPG and deep Q-network DQN,the average application delay of the proposed strategy is reduced by 30.29％and 54.11％,while the average energy consumption is reduced by 38.76％and 57.12％respectively.关键词
车联网/空天地一体化网络/计算卸载/深度强化学习/车载智能应用/车载边缘计算Key words
Internet of Vehicles/space-air-ground integrated network/computation offloading deep reinforcement learning/intelligent vehicular applications/vehicular edge computing分类
信息技术与安全科学引用本文复制引用
龚忠友,陈翌鸣,王洪滔,林兵..空天辅助车载边缘计算中基于深度强化学习的计算卸载策略[J].福建师范大学学报（自然科学版）,2026,42(2):22-32,11.基金项目
国家自然科学基金项目(62072108) （62072108）
福建省科技经济融合服务平台项目(2023XRH001) （2023XRH001）
福厦泉国家自主创新示范区协同创新平台项目(2022FX5) （2022FX5）
福建省高校产学合作资助项目(2022H6024、2021H6026) （2022H6024、2021H6026）
