基于深度强化学习的交通信号分布式控制合作伙伴选择研究

Shinya Matsuta, Naoki Kodama, Taku Harada
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引用次数: 1

摘要

交通信号控制是缓解道路网络交通拥挤的一种方法。交通信号控制的主要方法是信号局部协作的分布式控制方法。在本研究中,为了在分布式控制系统中实现更有效的控制,我们提出了一个选择各个交通信号合作伙伴的准则,并验证了其有效性。在本研究中,通过应用深度强化学习来控制交通信号,这是一种机器学习算法。
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Proposal for Selecting a Cooperation Partner in Distributed Control of Traffic Signals using Deep Reinforcement Learning
Traffic signal control is one way to alleviate traffic congestion on road networks. The main method of traffic signal control is a distributed control method in which signals cooperate locally. In this study, to realize more effective control in the distributed control system, we propose a guideline for selecting the cooperation partner of each traffic signal and verify its effectiveness. In this study, the traffic signal is controlled by applying deep reinforcement learning, which is a machine-learning algorithm.
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