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%.

中文翻译：

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半监督双决斗广泛强化学习以支持智慧城市的交通服务

交通服务是智慧城市的重要组成部分，会影响市民的出行质量。由于交通场景复杂多变的特点，快速准确的建模和处理要求对于交通服务（例如交通拥堵解决方案）至关重要。传统的深度强化学习（DRL）方法可以自主进行决策，但是其复杂的网络结构导致耗时的培训和更新过程。另外，在现实生活中提供大量标记数据并不总是可行的。为了解决这些问题，本研究提出了一种基于广义强化学习（BRL）的半监督双决斗广义强化学习（semi-DDBRL）方法。它对BRL进行了一些算法上的改进，例如对决网络和双重问 -学习网络，并添加半监督学习以提高建模和决策的准确性。作为智能城市应用的案例研究，作者将建议的Semi-DDBRL方法应用于交通拥堵问题。根据实验，他们的方法比DRL方法具有更快的执行时间。而且，与BRL方法相比，他们的方法可以将性能提高11.7％。