An Intelligent Traffic Light Control Algorithm Based on Deep Reinforcement Learning and Multi-task Learning
YANG Zhichao
KONG Yan
LU Xueliang
LI Ying
Abstract：In recent years,with the development of cities and transportation,alleviating traffic congestion has become a hot topic.At the same time,more and more researches on intelligent traffic lights have emerged.This paper proposes a new intelligent traffic light control algorithm TaskLight,which combines the idea of multi-task learning with the original DQN framework.In addi-tion,noise mechanism is introduced into the framework of multi-task learning to balance the importance of each task in each time period.The experimental evaluation results on real data-sets and synthetic data-sets show that compared with some of the most ad-vanced algorithms,TaskLight can significantly shorten the average travel time of vehicles and increase the throughput at intersec-tions.At the same time,TaskLight can make the main network converge to a better state by learning different tasks.In addition,the design of the algorithm framework is not limited to the field of intelligent traffic lights,and theoretically it is also transferable to oth-er areas of reinforcement learning.
Keywords：deep reinforcement learningintelligent traffic light controlmulti-task learningnoise mechanism
Publication Date：2025-12-20
Online Publishing Date：2026-03-09(First online date of this platform, not the publication date of the document)
Pages：5( 3342-3346 )