MARVEL:Multi-Agent Reinforcement Learning for VANET Delay Minimization  

作　　者：Chengyue Lu Zihan Wang Wenbo Ding Gang Li Sicong Liu Ling Cheng 

机构地区：[1]Tsinghua-Berkeley Shenzhen Institute,Tsinghua Shenzhen International Graduate School,Tsinghua University,518055,China [2]Department of Electronic Engineering,Tsinghua University,100084,China [3]Department of Information and Communication Engineering,Xiamen University,361005,China [4]School of Electrical and Information Engineering,University of the Witwatersrand,Johannesburg,2000,South Africa

出　　处：《China Communications》2021年第6期1-11,共11页中国通信（英文版）

基　　金：This work is supported by the National Science Foundation of China under grant No.61901403,61790551,and 61925106,Youth Innovation Fund of Xiamen No.3502Z20206039 and Tsinghua-Foshan Innovation Special Fund(TFISF)No.2020THFS0109.

摘　　要：In urban Vehicular Ad hoc Networks(VANETs),high mobility of vehicular environment and frequently changed network topology call for a low delay end-to-end routing algorithm.In this paper,we propose a Multi-Agent Reinforcement Learning(MARL)based decentralized routing scheme,where the inherent similarity between the routing problem in VANET and the MARL problem is exploited.The proposed routing scheme models the interaction between vehicles and the environment as a multi-agent problem in which each vehicle autonomously establishes the communication channel with a neighbor device regardless of the global information.Simulation performed in the 3GPP Manhattan mobility model demonstrates that our proposed decentralized routing algorithm achieves less than 45.8 ms average latency and high stability of 0.05%averaging failure rate with varying vehicle capacities.

关 键 词：VANET multi-agent RL delay minimization routing algorithm 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]