Abstract: The control of traffic signals is always a hotspot in intelligent transportation systems research.In order to adapt and coordinate traffic more timely and effectively,a novel traffic signal control algorithm based on distributional deep reinforcement learning was proposed.The model utilizes a deep neural network framework composed of target network,double Q network and value distribution to improve the performance.After integrating the discretization of the high-dimensional real-time traffic information at intersections with waiting time,queue length,delay time and phase information as states and making appropriate definitions of actions,rewards in the algorithm,it can learn the control strategy of traffic signals online and realize the adaptive control of traffic signals.It was compared with three typical deep reinforcement learning algorithms,and the experiments were performed in SUMO (Simulation of Urban Mobility) with the same setting.The results show that the distributional deep reinforcement learning algorithm is more efficient and robust,and has better performance on average delay,travel time,queue length,and wai-ting time of vehicles.

Key words: Deep reinforcement learning, Distributional reinforcement learning, Intelligent transportation, Traffic signal control