Deep Q Learning with LSTM for Traffic Light Control

Abstract

Most Conventional traffic light control (TLC) techniques do not provide enough efficiency to control dynamic traffic situations in real-time. Recently, DQN (Deep Q Network) algorithm is considered for TLC at the intersection because of its optimization technique for complex problems, where key features of the intersection traffic, such as vehicle positions and velocities, are obtained from the intersection by the camera installed at well above the ground. However, the general DQN-based TLC algorithms have failed to utilize the fact that vehicle trajectories are continuous, which can be very useful in sensing real-time traffic. To utilize the continuous vehicle motion for TLC improvement, we propose DRQN-TLC (Deep Recurrent Q Network for TLC) algorithm that is based on LSTM (Long-Short Term Memory) with DQN. The superior performance of the proposed algorithm is demonstrated with the simulation; the proposed algorithm reduces the average traveling time by 23% and the overall vehicle waiting time by 10% when compared with the general DQN-based TLC algorithm.