Abstract: “Existing traffic light controls are ineffective and causes a handful of problems such as congestion and pollution. The purpose of this study is to investigate the application of deep reinforcement learning on traffic control systems to minimize congestion at a targeted traffic intersection. The traffic data was extracted, analyzed and simulated based on the Poisson Distribution, using a simulator, Simulation of Urban Mobility (SUMO). In this research, we proposed a deep reinforcement learning model, which combines the capabilities of convolutional neural networks and reinforcement learning to control the traffic lights to increase the effectiveness of the traffic control system. The paper explains the method we used to quantify the traffic scenario into different matrices which fed to the model as states which reduces the load of computing as compared to images. After 2000 iterations of training, our deep reinforcement learning model was able to reduce the cumulative waiting time of al”

Keywords: Traffic Light Control, Deep Reinforcement Learning, SUMO