Fairness-aware multi-agent reinforcement learning and visual perception for adaptive traffic signal control  

作　　者：FANG Wanqing ZHAO Xintian ZHANG Chengwei 

机构地区：[1]Information Science and Technology College,Dalian Maritime University,Dalian 116026,China

出　　处：《Optoelectronics Letters》2024年第12期764-768,共5页光电子快报（英文版）

摘　　要：The majority of multi-agent reinforcement learning(MARL)methods for solving adaptive traffic signal control(ATSC)problems are dedicated to maximizing the throughput while ignoring fairness,resulting in a bad situation where some vehicles keep waiting.For this reason,this paper models the ATSC problem as a partially observable Markov game(POMG),in which a value function that combines throughput and fairness is elaborated.On this basis,we propose a new cooperative MARL method of fairness-aware multi-agent proximity policy optimization(FA-MAPPO).In addition,the FA-MAPPO uses graph attention neural networks to efficiently extract state representations from traffic data acquired through visual perception in multi-intersection scenarios.Experimental results in Jinan and synthetic scenarios confirm that the FA-MAPPO improves fairness while guaranteeing passage efficiency compared to the state-of-the-art(SOTA)methods.

关 键 词：ATSC AGENT VISUAL 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] U491.54[自动化与计算机技术—控制科学与工程]