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Efficient traffic signal control is critical for reducing traffic congestion and improving overall transportation efficiency.
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Sanh, V.; Debut, L.; Chaumond, J.; and Wolf, T. 2019 · 1910
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Learning an interpretable traffic signal control policy
Ault, J.; Hanna, J. P.; and Sharon, G. 2019 · 1912
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Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
Liu, H.; Tam, D.; Muqeeth, M.; Mohta, J.; Huang, T.; Bansal, M.; and Raffel, C. A. 2022 · 1965
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Traffic engineering
Roess, R. P.; Prassas, E. S.; and McShane, W. R. 2004 · 2004
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Integrierte Mikro-Simulation von Raum-und Verkehrsentwicklung: Theorie, Konzepte, Modelle, Praxis; Tagungsband zum 7. Aachener Kolloquium” Mobilität und Stadt”
Beckmann, K. J. 2006 · 2006
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InTAS–The Ingolstadt Traffic Scenario for SUMO
Lobo, S. C.; Neumeier, S.; Fernandez, E. M.; and Facchi, C. 2020 · 2011
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The max-pressure controller for arbitrary networks of signalized intersections
Varaiya, P. 2013 · 2013
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Distilling the knowledge in a neural network
Hinton, G.; Vinyals, O.; and Dean, J. 2015 · 2015
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Deep learning
Goodfellow, I.; Bengio, Y.; and Courville, A. 2016 · 2016
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Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
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Microscopic traffic simulation using sumo
Lopez, P. A.; Behrisch, M.; Bieker-Walz, L.; Erdmann, J.; Flötteröd, Y.-P.; Hilbrich, R.; Lücken, L.; Rummel, J.; Wagner, P.; and Wießner, E. 2018 · 2018
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Improving language understanding by generative pre-training
Radford, A.; Narasimhan, K.; Salimans, T.; Sutskever, I.; et al. 2018 · 2018
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Reinforcement learning: An introduction
Sutton, R. S.; and Barto, A. G. 2018 · 2018
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Multi-agent deep reinforcement learning for large-scale traffic signal control
Chu, T.; Wang, J.; Codecà, L.; and Li, Z. 2019 · 2019
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Parameter-efficient transfer learning for NLP
Houlsby, N.; Giurgiu, A.; Jastrzebski, S.; Morrone, B.; De Laroussilhe, Q.; Gesmundo, A.; Attariyan, M.; and Gelly, S. 2019 · 2019
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Language models are unsupervised multitask learners
Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; Sutskever, I.; et al. 2019 · 2019
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Toward a thousand lights: Decentralized deep reinforcement learning for large-scale traffic signal control
Chen, C.; and Wei, H. e. a. 2020 · 2020
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Feudal multi-agent deep reinforcement learning for traffic signal control
Ma, J.; and Wu, F. 2020 · 2020
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AdapterHub: A Framework for Adapting Transformers
Pfeiffer, J.; and Rücklé, e. a., Andreas. 2020 · 2020
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Transformers: State-of-the-Art Natural Language Processing
Wolf, T.; Debut, L.; Sanh, V.; Chaumond, J.; Delangue, C.; Moi, A.; Cistac, P.; Rault, T.; Louf, R.; Funtowicz, M.; Davison, J.; Shleifer, S.; von Platen, P.; Ma, C.; Jernite, Y.; Plu, J.; Xu, C.; Scao, T. L.; Gugger, S.; Drame, M.; Lhoest, Q.; and Rush, A. M. 2020 · 2020
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Metalight: Value-based meta-reinforcement learning for traffic signal control
Zang, X.; Yao, H.; Zheng, G.; Xu, N.; Xu, K.; and Li, Z. 2020 · 2020
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Reinforcement learning benchmarks for traffic signal control
Ault, J.; and Sharon, G. 2021 · 2021
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Decision transformer: Reinforcement learning via sequence modeling
Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Method
Jiang, Q.; Qin, M.; Shi, S.; Sun, W.; and Zheng, B. 2022 · 2022
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Offline Reinforcement Learning for Road Traffic Control
Kunjir, M.; and Chawla, S. 2022 · 2022
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Transformers are meta-reinforcement learners
Melo, L. C. 2022 · 2022
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Reinforcement learning in urban network traffic signal control: A systematic literature review
Noaeen, M.; Naik, A.; Goodman, L.; Crebo, J.; Abrar, T.; Abad, Z. S. H.; Bazzan, A. L.; and Far, B. 2022 · 2022
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Integrating public transit signal priority into max-pressure signal control: Methodology and simulation study on a downtown network
Xu, T.; Barman, S.; Levin, M. W.; Chen, R.; and Li, T. 2022 · 2022
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Chen, L.; Lu, K.; Rajeswaran, A.; Lee, K.; Grover, A.; Laskin, M.; Abbeel, P.; Srinivas, A.; and Mordatch, I. 2021 · 2021
Cited alongside, same era.
Traffic Signal Control Using Offline Reinforcement Learning
Dai, X.; Zhao, C.; Li, X.; Wang, X.; and Wang, F.-Y. 2021 · 2021
Cited alongside, same era.
Towards a Unified View of Parameter-Efficient Transfer Learning
He, J.; Zhou, C.; Ma, X.; Berg-Kirkpatrick, T.; and Neubig, G. 2021 · 2021
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Hu, E. J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; and Chen, W. 2021 · 2021
Cited alongside, same era.
ModelLight: Model-Based Meta-Reinforcement Learning for Traffic Signal Control
Huang, X.; Wu, D.; Jenkin, M.; and Boulet, B. 2021 · 2021
Cited alongside, same era.
Dynamic lane traffic signal control with group attention and multi-timescale reinforcement learning
Jiang, Q.; Li, J.; SUN, W. S.; and Zheng, B. 2021 · 2021
Cited alongside, same era.
Compacter: Efficient low-rank hypercomplex adapter layers
Karimi Mahabadi, R.; Henderson, J.; and Ruder, S. 2021 · 2021
Cited alongside, same era.
Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control
Zhang, L.; Wu, Q.; Shen, J.; Lü, L.; Du, B.; and Wu, J. 2022 · 2022
Later among the works it cites.
Online decision transformer
Zheng, Q.; Zhang, A.; and Grover, A. 2022 · 2022
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MTLight: Efficient Multi-Task Reinforcement Learning for Traffic Signal Control
Zhu, L.; Peng, P.; Lu, Z.; and Tian, Y. 2022 · 2022
Later among the works it cites.
Uncertainty-aware model-based offline reinforcement learning for automated driving
Diehl, C.; Sievernich, T. S.; Krüger, M.; Hoffmann, F.; and Bertram, T. 2023 · 2023
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Safelight: A reinforcement learning method toward collision-free traffic signal control
Du, W.; Ye, J.; Gu, J.; Li, J.; Wei, H.; and Wang, G. 2023 · 2023
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Benchmarking offline reinforcement learning on real-robot hardware
Gürtler, N.; Blaes, S.; Kolev, P.; Widmaier, F.; Wüthrich, M.; Bauer, S.; Schölkopf, B.; and Martius, G. 2023 · 2023
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A Survey on Transformers in Reinforcement Learning
Li, W.; Luo, H.; Lin, Z.; Zhang, C.; Lu, Z.; and Ye, D. 2023 · 2023
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Transformers are sample efficient world models
Micheli, V.; Alonso, E.; and Fleuret, F. 2023 · 2023
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A survey on offline reinforcement learning: Taxonomy, review, and open problems
Prudencio, R. F.; Maximo, M. R.; and Colombini, E. L. 2023 · 2023
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Traffic signal control using a cooperative EWMA-based multi-agent reinforcement learning
Qiao, Z.; Ke, L.; and Wang, X. 2023 · 2023
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Hierarchical graph multi-agent reinforcement learning for traffic signal control
Yang, S. 2023 · 2023
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