Traffic Signal Control Based on Reinforcement Learning and Fuzzy Neural Network

Abstract

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.