Adaptive Traffic Signal Control with Deep Recurrent Q-learning

Abstract

The application of modern technologies makes it possible for a transportation system to collect real-time data of some specific traffic scenes, helping traffic control center to improve the traffic efficiency. Based on such consideration, we introduce a variant deep reinforcement learning agent that might take advantage of the real-time GPS data and learn how to control the traffic lights in an isolated intersection. We combine the recurrent neural network (RNN) with Deep Q-Network, namely DRQN and compare its performance with standard Deep Q-Network (DQN) in partially observed traffic situations. The agent is trained by using Q-learning with experience replay in traffic simulator SUMO, so as to generate traffic signal control policy. Based on the experiments, both DQN and DRQN method are able to adjust its traffic signal timing policy to specific traffic environment and achieve lower average vehicle delay than fixed time control. In addition, the recurrent Q-learning method gets better simulation result than standard Q-learning method in the environment of different probe vehicle proportion.