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    Deep Reinforcement Learning for Addressing Disruptions in Traffic Light Control

    Faizan Rasheed1, Kok-Lim Alvin Yau2, Rafidah Md Noor3, Yung-Wey Chong4,*

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2225-2247, 2022, DOI:10.32604/cmc.2022.022952

    Abstract This paper investigates the use of multi-agent deep Q-network (MADQN) to address the curse of dimensionality issue occurred in the traditional multi-agent reinforcement learning (MARL) approach. The proposed MADQN is applied to traffic light controllers at multiple intersections with busy traffic and traffic disruptions, particularly rainfall. MADQN is based on deep Q-network (DQN), which is an integration of the traditional reinforcement learning (RL) and the newly emerging deep learning (DL) approaches. MADQN enables traffic light controllers to learn, exchange knowledge with neighboring agents, and select optimal joint actions in a collaborative manner. A case study based on a real traffic… More >

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