TY - JOUR
T1 - Dueling Double Deep Q-Network for Adaptive Traffic Signal Control with Low Exhaust Emissions in A Single Intersection
AU - Fang, Shu
AU - Chen, Feng
AU - Liu, Hongchao
N1 - Funding Information:
This work was funded by The Key Research & Development Project of Anhui Province (1804b06020376) and The National Key Research & Development Program of China (2017YFC0840206) respectively.
Publisher Copyright:
© 2019 Published under licence by IOP Publishing Ltd.
PY - 2019/10/21
Y1 - 2019/10/21
N2 - In order to reduce traffic exhaust emissions caused by the large quantities of vehicles, this paper studied the traffic signal control (TSC) model with low exhaust emissions on the basis of the deep reinforcement learning. In this study, the Dueling Double DQN with prioritized replay (DDDQN-PR) algorithm we proposed was combined with the Double DQN, Dueling DQN, and prioritized replay to achieve the goal of low exhaust emissions of TSC. The agent was trained in traffic simulator USTCMTS2.1 in a single intersection. The experimental results show that the performance of DDDQN-PR was significantly better than the other four algorithms, not only in data efficiency but also in final performance.
AB - In order to reduce traffic exhaust emissions caused by the large quantities of vehicles, this paper studied the traffic signal control (TSC) model with low exhaust emissions on the basis of the deep reinforcement learning. In this study, the Dueling Double DQN with prioritized replay (DDDQN-PR) algorithm we proposed was combined with the Double DQN, Dueling DQN, and prioritized replay to achieve the goal of low exhaust emissions of TSC. The agent was trained in traffic simulator USTCMTS2.1 in a single intersection. The experimental results show that the performance of DDDQN-PR was significantly better than the other four algorithms, not only in data efficiency but also in final performance.
UR - http://www.scopus.com/inward/record.url?scp=85074404541&partnerID=8YFLogxK
U2 - 10.1088/1757-899X/612/5/052039
DO - 10.1088/1757-899X/612/5/052039
M3 - Conference article
AN - SCOPUS:85074404541
VL - 612
JO - IOP Conference Series: Materials Science and Engineering
JF - IOP Conference Series: Materials Science and Engineering
SN - 1757-8981
IS - 5
M1 - 052039
Y2 - 22 June 2019 through 23 June 2019
ER -