城市交通的模拟与优化

Marco A. Wiering, J. Vreeken, J. V. Veenen, A. Koopman
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引用次数: 159

摘要

最优交通灯控制是一个多智能体决策问题,我们提出使用强化学习算法来解决这个问题。我们的算法学习了每个十字路口的车辆等待红灯和绿灯的预期时间,并将交通灯设置为绿色,以实现个体车辆收益最大化的配置。为了测试我们的自适应交通灯控制器,我们开发了绿灯区模拟器。实验结果表明,与手工设计的三种控制器相比,自适应算法能显著减少车辆的平均等待时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Simulation and optimization of traffic in a city
Optimal traffic light control is a multi-agent decision problem, for which we propose to use reinforcement learning algorithms. Our algorithm learns the expected waiting times of cars for red and green lights at each intersection, and sets the traffic lights to green for the configuration maximizing individual car gains. For testing our adaptive traffic light controllers, we developed the green light district simulator. The experimental results show that the adaptive algorithms can strongly reduce average waiting times of cars compared to three hand-designed controllers.
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