Design and realization of a new architecture based on multi-agent systems and reinforcement learning for traffic signal control

Maha Rezzai, W. Dachry, F. Moutaouakkil, H. Medromi
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引用次数: 6

Abstract

Increasing the number of cars in cities creates traffic congestion. This is due to static management of traffic lights. Reinforcement Learning RL algorithm is an artificial intelligence approach that enables adaptive real-time control at intersections. In this research paper, we purpose a new architecture based on multi-agent systems and RL algorithm in order to make the signal control system more autonomous, able to learn from its environment and make decisions to optimize road traffic.
基于多智能体系统和强化学习的交通信号控制新体系结构的设计与实现
城市中汽车数量的增加造成了交通堵塞。这是由于交通灯的静态管理。强化学习RL算法是一种人工智能方法,可以实现十字路口的自适应实时控制。在本文中,我们提出了一种基于多智能体系统和强化学习算法的新体系结构,以使信号控制系统更加自主,能够从其环境中学习并做出决策以优化道路交通。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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