Traffic signal optimization control method based on adaptive weighted averaged double deep Q network-Reference-Cited by-同舟云学术

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Traffic signal optimization control method based on adaptive weighted averaged double deep Q network

Published:2023-01-27 Issue:15 Volume:53 Page:18333-18354
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Chen Youqing,Zhang Huizhen,Liu Minglei,Ye Ming,Xie Hui,Pan Yubiao

Funder

Fujian Province Science and Technology Plan

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-023-04469-9.pdf

Reference44 articles.

1. Yu C, Feng Y, Liu HX, Ma W, Yang X (2018) Integrated optimization of traffic signals and vehicle trajectories at isolated urban intersections. Transp Res B Methodol 112:89–112. https://doi.org/10.1016/j.trb.2018.04.007

2. Yau K-LA, Qadir J, Khoo HL, Ling MH, Komisarczuk P (2017) A survey on reinforcement learning models and algorithms for traffic signal control. ACM Comput Surveys (CSUR) 50(3):1–38. https://doi.org/10.1145/3068287

3. Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G et al (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529–533. https://doi.org/10.1038/nature14236

4. Wu D, Dong X, Shen J, Hoi SC (2020) Reducing estimation bias via triplet-average deep deterministic policy gradient. IEEE Trans Neural Netw Learn Syst 31(11):4933–4945. https://doi.org/10.1109/TNNLS.2019.2959129

5. Van Hasselt H, Guez A, Silver D (2016) Deep reinforcement learning with double q-learning. In: Proceedings of the AAAI Conference on Artificial Intelligence. AAAI, vol 30, pp 2094–2100. https://doi.org/10.1609/aaai.v30i1.10295

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