无线电工程2024，Vol.54Issue(6)：1388-1397,10.DOI:10.3969/j.issn.1003-3106.2024.06.007
基于多智能体深度强化学习的车联网资源分配方法
Resource Allocation for Vehicular Networking Based on Multi-agent Deep Reinforcement Learning
摘要
Abstract
In vehicular networks,the rational allocation of spectrum resources is of great importance in meeting the Quality of Service(QoS)requirements for diverse vehicular link services.To address challenges such as high vehicular mobility and difficulties in obtaining global state information,a resource allocation algorithm based on fully distributed Multi-Agent Deep Reinforcement Learning(MADRL)is proposed.With vehicle communication delays and reliability taken into account,the network throughput is maximized by optimizing spectrum selection and power allocation strategies.Firstly,a shared experience pool mechanism is introduced to tackle the non-stationarity issues caused by concurrent multi-agent learning.Secondly,dynamic environmental information is captured and utilized by a Deep Q Network(DQN)built upon Long Short Term Memory(LSTM)networks,addressing the challenge of partially observable environments for agents.Finally,he training accuracy and predictive capabilities of the algorithm are enhanced by integrating Convolutional Neural Network(CNN)and Residual Network(ResNet).Experimental results demonstrate that the proposed algorithm is capable of meeting the high throughput requirements of Vehicle-to-Infrastructure(V2I)links and low latency requirements of Vehicle-to-Vehicle(V2V)links while showing adaptability to changing environments.关键词
车联网/资源分配/多智能体深度强化学习/深度Q网络Key words
vehicular network/resource allocation/MADRL/DQN分类
信息技术与安全科学引用本文复制引用
孟水仙,刘艳超,王树彬..基于多智能体深度强化学习的车联网资源分配方法[J].无线电工程,2024,54(6):1388-1397,10.基金项目
国家自然科学基金(62361048)National Natural Science Foundation of China(62361048) （62361048）
