Targeted knowledge transfer for learning traffic signal plans

Traffic signal control in cities today is not well optimized according to the feedback received from the real world. And such an inefficiency in traffic signal control results in people’s waste of time in commuting, road rage in the traffic jam, and high cost for city operation. Recently, deep reinforcement learning (DRL) approaches shed lights to better optimize traffic signal plans according to the feedback received from the environment. Most of these methods are evaluated in a simulated environment, but can not be applied to intersections in the real world directly, as the training of DRL relies on a great amount of samples and takes a long time to converge. In this paper, we propose a batch learning framework where the targeted transfer reinforcement learning (TTRL-B) is introduced to speed up learning. Specifically, a separate unsupervised method is designed to measure the similarities of traffic conditions to select the suitable source intersection for transfer. The proposed framework allows batch learning and this is the first work to consider the impact of slow learning in RL on real-world applications. Experiments on real traffic data demonstrate that our model accelerates learning with good performance.