Efficient Path Planning for Large-Scale Vehicular Networks via Multi-agent Mean Field Reinforcement Learning

Chen，Tian1,2; Chen，Wenbin1; Gao，Huifei1,3

10.1007/978-981-97-7001-4_16

2025

1865-0929

1865-0937

Communications in Computer and Information Science

2181 CCIS

219-233

While existing research has made progress in optimizing the route planning performance for a small number of vehicles, it often falls short of adequately considering the dynamic interactions and mutual influence among vehicles in large-scale vehicular networks. This deficiency limits its effectiveness in addressing urban traffic congestion and enhancing the overall efficiency of the transportation system. Therefore, in this paper, we propose a multi-agent mean field reinforcement learning (MAMFRL) framework for large-scale vehicle path planning problems, aiming to improve the efficiency of individual vehicles and the entire transportation system. Specifically, we first utilize mean field (MF) theory to simplify the interactions between agents. Second, MAMFRL employs a convolutional neural network (CNN) layer to extract road information features and obtain spatial correlations of urban traffic. Finally, MAMFRL constrains the rewards to improve the performance of the whole transportation system. Experimental results show that the proposed method can reduce average vehicle travel time by up to 9% and average intersection queue lengths by up to 27.8%.

deep reinforcement learning intelligent transportation system path planning traffic congestion

通讯

英语

2-s2.0-85205339148

Scopus

1.School of Computer Science and Artificial Intelligence,Wuhan University of Technology,Wuhan,China
2.Institute of Future Networks,Southern University of Science and Technology,Shenzhen,China
3.Linkinsense Co.,Ltd.,Hefei,China

Chen，Tian,Chen，Wenbin,Gao，Huifei. Efficient Path Planning for Large-Scale Vehicular Networks via Multi-agent Mean Field Reinforcement Learning[C],2025:219-233.