A cold-start-free reinforcement learning approach for traffic signal control-Reference-Cited by-同舟云学术

A cold-start-free reinforcement learning approach for traffic signal control

Published:2021-06-06 Issue: Volume: Page:1-10
ISSN:1547-2450
Container-title:Journal of Intelligent Transportation Systems
language:en
Short-container-title:Journal of Intelligent Transportation Systems

Author:

Xiao Nan¹,Yu Liang²,Yu Jinqiang²,Chen Peng²,Liu Yuehu³

Affiliation:

1. Alibaba Cloud Intelligence, Alibaba Group, Singapore, Singapore

2. Alibaba Cloud Intelligence, Alibaba Group, Hangzhou, China

3. Alibaba Cloud Intelligence, Alibaba Group, Beijing, China

Publisher

Informa UK Limited

Subject

Applied Mathematics,Computer Science Applications,Aerospace Engineering,Automotive Engineering,Information Systems,Software,Control and Systems Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/15472450.2021.1934679

Reference50 articles.

1. A rolling-horizon quadratic-programming approach to the signal control problem in large-scale congested urban road networks

2. Reinforcement learning-based multi-agent system for network traffic signal control

3. Baker, B., Gupta, O., Naik, N. & Raskar, R. (2016). Designing neural network architectures using reinforcement learning. arXiv preprint (arXiv:1611.02167).

4. Self-Organizing Traffic Lights: A Realistic Simulation

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