Learning Thresholds to Select Cooperative Partners by Applying Deep Reinforcement Learning in Distributed Traffic Signal Control

Shinya Matsuta, Naoki Kodama, Taku Harada
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Abstract

One method to reduce vehicle congestion in a road traffic network is to appropriately control traffic signals. One control scheme for traffic signals is a distributed control scheme in which individual traffic signals cooperate locally with other geographically close traffic signals. Deep reinforcement learning has been actively studied to appropriately control traffic signals. In distributed control, it is important to select appropriate cooperative partners. In this study, we propose a method for selecting appropriate cooperative partners using deep reinforcement learning to the distributed traffic signal control.
基于深度强化学习的分布式交通信号控制合作伙伴选择学习阈值
适当控制交通信号是减少道路交通网络中车辆拥塞的一种方法。交通信号的一种控制方案是分布式控制方案,其中单个交通信号与地理位置相近的其他交通信号局部合作。人们积极研究深度强化学习来适当地控制交通信号。在分布式控制中,选择合适的合作伙伴非常重要。在本研究中,我们提出了一种基于深度强化学习的分布式交通信号控制中选择合适合作伙伴的方法。
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CiteScore
1.60
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