Dueling Double Deep Q-Network for Adaptive Traffic Signal Control with Low Exhaust Emissions in A Single Intersection

Shu Fang, Feng Chen, Hongchao Liu

Research output: Contribution to journalConference articlepeer-review

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.

Original languageEnglish
Article number052039
JournalIOP Conference Series: Materials Science and Engineering
Volume612
Issue number5
DOIs
StatePublished - Oct 21 2019
Event2019 6th International Conference on Advanced Composite Materials and Manufacturing Engineering, ACMME 2019 - Xishuangbanna, Yunnan, China
Duration: Jun 22 2019Jun 23 2019

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