TY - GEN
T1 - Targeted knowledge transfer for learning traffic signal plans
AU - Xu, Nan
AU - Zheng, Guanjie
AU - Xu, Kai
AU - Zhu, Yanmin
AU - Li, Zhenhui
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85064951293&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85064951293&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-16145-3_14
DO - 10.1007/978-3-030-16145-3_14
M3 - Conference contribution
AN - SCOPUS:85064951293
SN - 9783030161446
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 175
EP - 187
BT - Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings
A2 - Huang, Sheng-Jun
A2 - Gong, Zhiguo
A2 - Yang, Qiang
A2 - Zhang, Min-Ling
A2 - Zhou, Zhi-Hua
PB - Springer Verlag
T2 - 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019
Y2 - 14 April 2019 through 17 April 2019
ER -