Abstract: Traffic lights play a vital role in controlling traffic at intersections. At present， the traffic lights at urban intersections mostly adopt the control strategy of fixed timing and fixed phase transformation， which is difficult to meet different traffic flow conditions. It has become one of the research hotspots in the field of intelligent transportation to design a control scheme that can adjust the traffic light transformation in real time according to the traffic flow at the intersection. However， the traffic flow at urban intersections is dynamic， so it is difficult to study it directly. In order to design an appropriate traffic light dynamic control scheme， the deep strong learning technology is introduced. The intersection traffic light control problem is abstracted into a reinforcement learning model， which is solved by D3QN algorithm. On this basis， considering the vehicles in different states， the state input and reward function are improved. Finally， the simulation experiments under different traffic flows are carried out on the traffic simulator SUMO. The experimental results show that after the model training becomes stable， the average queue length of the D3QN algorithm with improved reward function and state input is significantly improved compared with the traditional fixed control strategy and adaptive control strategy under three traffic flows， and the control effect is better then DQN algorithms and DDQN algorithms.

Key words: traffic light control, deep reinforcement learning, D3QN algorithm, status input, reward function, traffic flow