CoSLight: Co-optimizing Collaborator Selection and Decision-making to Enhance Traffic Signal Control-Reference-Cited by-同舟云学术

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CoSLight: Co-optimizing Collaborator Selection and Decision-making to Enhance Traffic Signal Control

Published:2024-08-24 Issue: Volume:96 Page:2500-2511
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Ruan Jingqing¹^ORCID,Li Ziyue²^ORCID,Wei Hua³^ORCID,Jiang Haoyuan⁴^ORCID,Lu Jiaming⁵^ORCID,Xiong Xuantang¹^ORCID,Mao Hangyu⁶^ORCID,Zhao Rui⁷^ORCID

Affiliation:

1. Institute of Automation, Chinese Academy of Science, Chinese Academy of Sciences, Beijing, China

2. University of Cologne, EWI gGmbH, Cologne, Germany

3. Arizona State University, Arizona, USA

4. Baidu Inc., Shenzhen, China

5. Fudan University, Shanghai, China

6. Peking University, Beijing, China

7. Qing Yuan Research Institute, Shanghai Jiao Tong University, Shanghai, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671998

Reference67 articles.

1. Ault, J., Hanna, J. P., and Sharon, G. Learning an interpretable traffic signal control policy. arXiv preprint arXiv:1912.11023 (2019).

2. Ault, J., and Sharon, G. Reinforcement learning benchmarks for traffic signal control. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1) (2021).

3. Dynamic Programming

4. Böhmer, W., Kurin, V., and Whiteson, S. Deep coordination graphs. In International Conference on Machine Learning (2020), PMLR, pp. 980--991.

5. Boutilier, C. Planning, learning and coordination in multiagent decision processes. In TARK (1996), vol. 96, pp. 195--210.

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