Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IOP Conference Series: Materials Science and Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
