Biased Pressure: Cyclic Reinforcement Learning Model for Intelligent Traffic Signal Control

Existing inefficient traffic signal plans are causing traffic congestions in many urban areas. In recent years, many deep reinforcement learning (RL) methods have been proposed to control traffic signals in real‐time by interacting with the environment. However, most of existing state‐of-the‐art RL...

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Bibliographic Details
Main Authors:	Ibrokhimov, B. (Author), Kang, S. (Author), Kim, Y.-J (Author)
Format:	Article
Language:	English
Published:	MDPI 2022
Subjects:	article Cyclic phasis Deep learning intelligent traffic signal control Intelligent traffic signal control Intelligent traffics learning Optimisations optimization Pressure methods reinforcement (psychology) reinforcement learning Reinforcement learning Reinforcement learning method Reinforcement learning models reward State of the art Street traffic control Traffic congestion Traffic signal control Traffic signals travel Travel time Urban areas
Online Access:	View Fulltext in Publisher

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View Fulltext in Publisher

Biased Pressure: Cyclic Reinforcement Learning Model for Intelligent Traffic Signal Control

Internet

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