mlsanityData to 7 Oct 2026

DynLight: Realize dynamic phase duration with multi-level traffic signal control

arXiv 2204.03471 ↗First posted 7 Apr 2022cs.AI cs.LG cs.SYcited by 0

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Abstract (arXiv)

We would like to withdraw this article for the following reasons: 1 this article is not satisfactory for limited language and theoretical description; 2 we have enriched and revised this article with the help of other authors; 3 we must update the author contribution information.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 34 references that are in the map. First posted on arXiv 7 Apr 2022.

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15 of 34 references.

References · 15

  1. 2017Attention Is All You Needcited by 59,932
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  4. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  5. 2016Generative Adversarial Imitation Learningcited by 1,576
  6. 2016Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivationcited by 282
  7. 2019CoLight: Learning Network-level Cooperation for Traffic Signal Controlcited by 133
  8. 2019CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenariocited by 114
  9. 2019Learning Phase Competition for Traffic Signal Controlcited by 84
  10. 2019A Survey on Traffic Signal Control Methodscited by 78
  11. 2020AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Controlcited by 60
  12. 2018Deep Reinforcement Learning for Traffic Light Control in Vehicular Networkscited by 51
3 more
  1. 2019Diagnosing Reinforcement Learning for Traffic Signal Controlcited by 33
  2. 2021Efficient Pressure: Improving efficiency for signalized intersectionscited by 24
  3. 2013Decentralized Signal Control for Urban Road Networkscited by 7

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