Intelligent traffic light via reinforcement learning

People travel with vehicles daily. The traffic light is one of the most critical signals in an intersection to prevent accidents and help all vehicles on all the roads pass the intersection safely and efficiently. However, most traffic lights used today are fixed-time traffic lights. The logic is predefined and sometimes this approach is inefficient and wastes drivers’ time. This research focuses on finding an optimal operation strategy for a traffic light to make it more efficient to pass the traffic flow. One of the artificial intelligence (AI) techniques, reinforcement learning, is employed to train the control agent that can optimally choose the traffic light phase. Consequently, the designed traffic light is intelligent. We use three reinforcement learning methods, including Deep Q-learning (DQN), Double Deep Q-Learning (DDQN), and Proximal Policy Optimization (PPO). The generated policies are compared, and PPO is found to be the best method to train the intelligent traffic light. Then, PPO is utilized to further design an intelligent traffic light, considering the light phases with variable intervals. In this design, the control agent can choose a traffic light phase and a time interval for the phase to last. Furthermore, environment and action disturbances are considered. Environment disturbance is a probability that traffic collisions may happen. Action disturbance is a probability that the traffic light may malfunction and switch its phase randomly. The results demonstrate that the intelligent traffic light is robust and has high efficiency.