A General Scenario-Agnostic Reinforcement Learning for Traffic Signal Control
Author:
Affiliation:
1. Sense-Time Research, Beijing, China
2. Department of Information Systems, University of Cologne, Köln, Germany
3. Shanghai AI Laboratory, Shanghai, China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/6979/10659279/10481508.pdf?arnumber=10481508
Reference44 articles.
1. Recent Advances in Reinforcement Learning for Traffic Signal Control
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3. The SCOOT online traffic signal optimisation technique;Hunt;Traffic Eng. Control,1982
4. SCATS, Sydney co-ordinated adaptive traffic system: A traffic responsive method of controlling urban traffic;Lowrie,1990
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1. FELight: Fairness-Aware Traffic Signal Control via Sample-Efficient Reinforcement Learning;IEEE Transactions on Knowledge and Data Engineering;2024-09
2. CoSLight: Co-optimizing Collaborator Selection and Decision-making to Enhance Traffic Signal Control;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24
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