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Open AccessArticle

An Edge Based Multi-Agent Auto Communication Method for Traffic Light Control

1
School of Information & Engineering, Lanzhou University, Lanzhou 730000, China
2
School of Computing and Information Technology, University of Wollongong, Wollongong 2522, Australia
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(15), 4291; https://doi.org/10.3390/s20154291
Received: 30 June 2020 / Revised: 24 July 2020 / Accepted: 29 July 2020 / Published: 31 July 2020
(This article belongs to the Special Issue Internet of Things, Big Data and Smart Systems)
With smart city infrastructures growing, the Internet of Things (IoT) has been widely used in the intelligent transportation systems (ITS). The traditional adaptive traffic signal control method based on reinforcement learning (RL) has expanded from one intersection to multiple intersections. In this paper, we propose a multi-agent auto communication (MAAC) algorithm, which is an innovative adaptive global traffic light control method based on multi-agent reinforcement learning (MARL) and an auto communication protocol in edge computing architecture. The MAAC algorithm combines multi-agent auto communication protocol with MARL, allowing an agent to communicate the learned strategies with others for achieving global optimization in traffic signal control. In addition, we present a practicable edge computing architecture for industrial deployment on IoT, considering the limitations of the capabilities of network transmission bandwidth. We demonstrate that our algorithm outperforms other methods over 17% in experiments in a real traffic simulation environment. View Full-Text
Keywords: ITS; IoT; reinforcement learning; MRAL; multi-agent; MAAC; edge computing ITS; IoT; reinforcement learning; MRAL; multi-agent; MAAC; edge computing
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MDPI and ACS Style

Wu, Q.; Wu, J.; Shen, J.; Yong, B.; Zhou, Q. An Edge Based Multi-Agent Auto Communication Method for Traffic Light Control. Sensors 2020, 20, 4291.

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