mlsanityData to 7 Oct 2026

Large-Scale Traffic Signal Control Using Constrained Network Partition and Adaptive Deep Reinforcement Learning

arXiv 2303.11899 ↗First posted 21 Mar 2023cs.AIcited by 6

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Abstract (arXiv)

Multi-agent Deep Reinforcement Learning (MADRL) based traffic signal control becomes a popular research topic in recent years. To alleviate the scalability issue of completely centralized RL techniques and the non-stationarity issue of completely decentralized RL techniques on large-scale traffic networks, some literature utilizes a regional control approach where the whole network is firstly partitioned into multiple disjoint regions, followed by applying the centralized RL approach to each region. However, the existing partitioning rules either have no constraints on the topology of regions or require the same topology for all regions. Meanwhile, no existing regional control approach explores the performance of optimal joint action in an exponentially growing regional action space when intersections are controlled by 4-phase traffic signals (EW, EWL, NS, NSL). In this paper, we propose a novel RL training framework named RegionLight to tackle the above limitations. Specifically, the topology of regions is firstly constrained to a star network which comprises one center and an arbitrary number of leaves. Next, the network partitioning problem is modeled as an optimization problem to minimize the number of regions. Then, an Adaptive Branching Dueling Q-Network (ABDQ) model is proposed to decompose the regional control task into several joint signal control sub-tasks corresponding to particular intersections. Subsequently, these sub-tasks maximize the regional benefits cooperatively. Finally, the global control strategy for the whole network is obtained by concatenating the optimal joint actions of all regions. Experimental results demonstrate the superiority of our proposed framework over all baselines under both real and synthetic datasets in all evaluation metrics.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 11 of its 67 references that are in the map. First posted on arXiv 21 Mar 2023.

Builds on

11 of 67 references.

References · 11

  1. 2019Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithmscited by 555
  2. 2019Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Controlcited by 159
  3. 2018Graph Convolutional Reinforcement Learningcited by 148
  4. 2019CoLight: Learning Network-level Cooperation for Traffic Signal Controlcited by 133
  5. 2019CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenariocited by 114
  6. 2020Deep Reinforcement Learning for Intelligent Transportation Systems: A Surveycited by 94
  7. 2017Action Branching Architectures for Deep Reinforcement Learningcited by 90
  8. 2019A Survey on Traffic Signal Control Methodscited by 78
  9. 2019Diagnosing Reinforcement Learning for Traffic Signal Controlcited by 33
  10. 2019Large-Scale Traffic Signal Control Using a Novel Multi-Agent Reinforcement Learningcited by 33
  11. 2020IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Controlcited by 20

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Cited by year

Citing papers per year: 2024: 1; 2025: 5.

6 papers.

  1. 2024LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban Environmentscited by 31
  2. 2025Communication Strategy on Macro-and-Micro Traffic State in Cooperative Deep Reinforcement Learning for Regional Traffic Signal Controlcited by 1
  3. 2025Toward Dependency Dynamics in Multi-Agent Reinforcement Learning for Traffic Signal Controlcited by 0
  4. 2025Using a single actor to output personalized policy for different intersectionscited by 0
  5. 2025A Joint Topology-Data Fusion Graph Network for Robust Traffic Speed Prediction with Data Anomalismcited by 0
  6. 2025A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planningcited by 0