A General Scenario-Agnostic Reinforcement Learning for Traffic Signal Control

Reinforcement learning (RL) can automatically learn a better policy through a trial-and-error paradigm and has been adopted to revolutionize and optimize traditional traffic signal control systems that are usually based on handcrafted methods. However, most existing RL-based models are either based...

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Veröffentlicht in:	IEEE transactions on intelligent transportation systems 2024-09, Vol.25 (9), p.11330-11344
Hauptverfasser:	Jiang, Haoyuan, Li, Ziyue, Li, Zhishuai, Bai, Lei, Mao, Hangyu, Ketter, Wolfgang, Zhao, Rui
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptation models General model Labeling Manuals Reinforcement learning Testing traffic control Training Urban areas
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