Multi-intersections traffic signal intelligent control using collaborative q-learning algorithm

Chungui Li, Xianglei Yan, Fei-Ying Lin, Hongling Zhang
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引用次数: 8

Abstract

Since congestion of traffic is ubiquitous in the modern city, optimizing the behavior of traffic lights for efficient traffic flow is a critically important goal. However,agents often select only locally optimal actions without coordinating their neighbor intersections. In this paper, an urban road traffic area-wide coordination control algorithm based on collaborative Q-learning is proposed. The agent model of traffic intersections is demonstrated. The algorithm substantially reduces average vehicular delay by using a collaborative Q-learning algorithm and can cooperative control of multiple intersections to achieve a near optimal control policy. The computer simulation results show that the control algorithm can effectively reduce the average delay time and play a very good control effect with multi-intersections, so the coordination method used in this paper is effective.
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基于协同q-学习算法的多路口交通信号智能控制
由于现代城市中交通拥堵无处不在,优化交通信号灯的行为以实现高效的交通流是一个至关重要的目标。然而,智能体通常只选择局部最优行为,而不协调其相邻的交叉点。提出了一种基于协同q学习的城市道路交通全区域协调控制算法。对交通交叉口的智能体模型进行了论证。该算法采用协同q -学习算法大幅降低了车辆平均延误,并能对多个交叉口进行协同控制,达到接近最优的控制策略。计算机仿真结果表明,该控制算法可以有效地降低平均延迟时间,并在多路口情况下起到很好的控制效果,因此本文采用的协调方法是有效的。
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