Traffic signal control using reinforcement learning based on the teacher-student framework

Reinforcement Learning (RL) is an effective method for adaptive traffic signals control. As one type of RL, the teacher-student framework has been found helpful in improving the model performance for different application fields (such as robot control, game, hybrid intelligence), but it is rarely applied for traffic control due to that the hyper-parameters and the number of state-action pairs experienced are difficult to determine. In this work, the teacher-student framework is used for traffic signal control, where only a single reward function is designed to guide the student agent and by using this method the number of hyper-parameters and the model complexity are reduced. Specifically, the teacher agent uses an importance function to evaluate and guide the student, where the importance function combines with environment reward to form a synthetic reward for the student agent. Experimental results under different traffic environments show that the proposed method achieves the expected performance enhancement and is better than most of the state-of-the-art RL-based traffic signal control methods.

Automated summary

This study applies a reinforcement learning approach called the teacher-student framework to traffic signal control. By designing a single reward function to guide the student agent, the number of hyperparameters and model complexity are reduced. The teacher agent uses an importance function to evaluate and guide the student, and experimental results show that this method achieves improved performance compared to other state-of-the-art RL-based traffic signal control methods.