For traffic signal control of intersections in cities, a new controller based on reinforcement learning and fuzzy neural network is proposed in this paper. The fuzzy neural network has the advantages of both fuzzy control and neural network, and overcome the former's lack of self-learning and generalization ability, and the latter's lack of understandability. Meanwhile, the reinforcement learning can make the controller improve itself on line continually by the simple feedback of environment. The result of computational experiments shows that the proposed traffic signal control algorithm can achieve a more effective optimization control.


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    Title :

    Traffic Signal Control Based on Reinforcement Learning and Fuzzy Neural Network


    Contributors:
    Zhao, Hongxia (author) / Chen, Songhang (author) / Zhu, Fenghua (author) / Tang, Haina (author)


    Publication date :

    2022-10-08


    Size :

    572125 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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