Semi‐supervised double duelling broad reinforcement learning in support of traffic service in smart cities

Abstract

Traffic service is an important building block of smart cities, affecting citizens' travel quality. Due to the characteristics of complicated and volatile traffic scenarios, fast and accurate modelling and processing requirements are vital for traffic service, such as traffic congestion solutions. Traditional deep reinforcement learning (DRL) approach can make decisions autonomously, but its complex network structure leads to time-consuming training and updating processes. In addition, it is not always feasible to provide a large amount of tagged data in real life. To solve these problems, this study proposes a semi-supervised double duelling broad reinforcement learning (semi-DDBRL) approach based on the broad reinforcement learning (BRL). It incorporates some algorithmic improvements into the BRL, such as duelling network and double Q-learning network, and adds semi-supervised learning to improve the accuracy of modelling and decision making. As a case study of smart city applications, the authors apply the proposed semi-DDBRL approach to the problem of traffic congestion. Based on the experiments, their approach can have a faster execution time than the DRL approach. Moreover, compared with the BRL approach, their approach can improve the performance by 11.7%.