mlsanityData to 14 Oct 2026

Single-agent Reinforcement Learning Model for Regional Adaptive Traffic Signal Control

arXiv 2511.00551 ↗First posted 1 Nov 2025cs.AI cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic

Abstract (arXiv)

Several studies have employed reinforcement learning (RL) to address the challenges of regional adaptive traffic signal control (ATSC) and achieved promising results. In this field, existing research predominantly adopts multi-agent frameworks. However, the adoption of multi-agent frameworks presents challenges for scalability. Instead, the Traffic signal control (TSC) problem necessitates a single-agent framework. TSC inherently relies on centralized management by a single control center, which can monitor traffic conditions across all roads in the study area and coordinate the control of all intersections. This work proposes a single-agent RL-based regional ATSC model compatible with probe vehicle technology. Key components of the RL design include state, action, and reward function definitions. To facilitate learning and manage congestion, both state and reward functions are defined based on queue length, with action designed to regulate queue dynamics. The queue length definition used in this study differs slightly from conventional definitions but is closely correlated with congestion states. More importantly, it allows for reliable estimation using link travel time data from probe vehicles. With probe vehicle data already covering most urban roads, this feature enhances the proposed method's potential for widespread deployment. The method was comprehensively evaluated using the SUMO simulation platform. Experimental results demonstrate that the proposed model effectively mitigates large-scale regional congestion levels via coordinated multi-intersection control.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 9 of its 30 references that are in the map. First posted on arXiv 1 Nov 2025.

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9 of 30 references.

References · 9

  1. 2019Dream to Control: Learning Behaviors by Latent Imaginationcited by 1,653
  2. 2018World Modelscited by 1,513
  3. 2023Mastering Diverse Domains through World Modelscited by 1,253
  4. 2020Mastering Atari with Discrete World Modelscited by 1,007
  5. 2017A Brief Survey of Deep Reinforcement Learningcited by 483
  6. 2019Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Controlcited by 164
  7. 2020Deep Reinforcement Learning for Intelligent Transportation Systems: A Surveycited by 98
  8. 2019A Survey on Traffic Signal Control Methodscited by 79
  9. 2023Multi-Agent Reinforcement Learning Based on Representational Communication for Large-Scale Traffic Signal Controlcited by 7

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  1. 2025Robust Single-Agent Reinforcement Learning for Regional Traffic Signal Control Under Demand Fluctuations
  2. 2025Large-scale Regional Traffic Signal Control Based on Single-Agent Reinforcement Learning
  3. 2024Reinforcement Learning for Adaptive Traffic Signal Control: Turn-Based and Time-Based Approaches to Reduce Congestion
  4. 2025Reinforcement Learning Based Traffic Signal Design to Minimize Queue Lengths
  5. 2026A Robust and Efficient Multi-Agent Reinforcement Learning Framework for Traffic Signal Control
  6. 2023A Fully Data-Driven Approach for Realistic Traffic Signal Control Using Offline Reinforcement Learning
  7. 2025Adaptive Tuning of Parameterized Traffic Controllers via Multi-Agent Reinforcement Learning
  8. 2025A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal Control