基于A3C的车联网任务卸载和资源分配算法  

A3C Based Task Offloading and Resource Allocation Algorithm for Internet of Vehicles

作　　者：吴一川 WU Yichuan(School of Computer Science,Xi’an Polytechnic University,Xi’an 710600,China)

机构地区：[1]西安工程大学计算机科学学院,陕西西安710600

出　　处：《计算机与现代化》2025年第4期56-62,共7页Computer and Modernization

基　　金：陕西省科学技术厅一般项目(2023QCY-LL-34,2023QYPY-14);西安市科技局一般项目(2023JHQCYCK-0030);咸阳市科技局一般项目(L2022-ZDYF-GY-015)。

摘　　要：移动边缘计算(Mobile Edge Computing,MEC)作为一项新兴技术,为车联网应用提供新的解决方案。然而车联网环境中的资源有限,无法满足接入车联网的车辆设备的使用需求,这导致任务的服务响应时间和执行能耗增加,极大影响了用户的体验质量(Quality of Experience,QoE)。为了减少任务执行的延迟和能耗,提高算法部署的灵活性,本文对车联网系统模型进行建模并提出一种基于A3C(Asynchronous Advantage Actor-Critic)算法的任务卸载和资源分配策略,该算法框架采用异步更新的方式训练模型,并且加入时间衰减系数来减少落后模型对全局模型更新造成的不利影响。实验结果表明,本文所提算法可以有效提升模型训练效率,降低任务执行的延迟和能耗。Mobile Edge Computing(MEC),as a new technology,provides a new solution for the application of the Internet of Vehicles.However,the limited resources in the connected vehicle environment cannot meet the needs of the connected vehicle equipment,which leads to an increase in the service response time and execution energy consumption of tasks,which greatly affects the Quality of Experience(QoE)of users.In order to reduce the delay and energy consumption of task execution and improve the flexibility of algorithm deployment,this paper constructs the networked vehicle system model and proposes an asynchronous advantage actor-critic based task offloading and resource allocation strategy.The algorithm framework uses asynchronous updating to train the model,and adds time attenuation coefficient to reduce the adverse effect of backward model on global model updating.Experimental results show that the proposed algorithm can effectively improve model training efficiency and reduce task execution delay and energy consumption.

关 键 词：车联网 边缘计算 任务卸载 资源分配 深度强化学习 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]