CoordLight: Learning Decentralized Coordination for Network-Wide Traffic Signal Control

• URL: http://arxiv.org/abs/2603.24366v1
• Date: Wed, 25 Mar 2026 14:46:31 GMT
• Title: CoordLight: Learning Decentralized Coordination for Network-Wide Traffic Signal Control
• Abstract summary: CoordLight is a framework designed to improve intra-neighborhood traffic by enhancing decision-making at individual junctions (agents)<n>We propose an advanced MARL algorithm, named Neighbor-aware Policy Optimization (NAPO), which integrates an attention mechanism that discerns the state and action dependencies among adjacent agents.<n>We empirically show that CoordLight consistently exhibits superior performance across diverse traffic networks with varying traffic flows.
• Score: 15.468107943726617
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adaptive traffic signal control (ATSC) is crucial in alleviating congestion, maximizing throughput and promoting sustainable mobility in ever-expanding cities. Multi-Agent Reinforcement Learning (MARL) has recently shown significant potential in addressing complex traffic dynamics, but the intricacies of partial observability and coordination in decentralized environments still remain key challenges in formulating scalable and efficient control strategies. To address these challenges, we present CoordLight, a MARL-based framework designed to improve intra-neighborhood traffic by enhancing decision-making at individual junctions (agents), as well as coordination with neighboring agents, thereby scaling up to network-level traffic optimization. Specifically, we introduce the Queue Dynamic State Encoding (QDSE), a novel state representation based on vehicle queuing models, which strengthens the agents' capability to analyze, predict, and respond to local traffic dynamics. We further propose an advanced MARL algorithm, named Neighbor-aware Policy Optimization (NAPO). It integrates an attention mechanism that discerns the state and action dependencies among adjacent agents, aiming to facilitate more coordinated decision-making, and to improve policy learning updates through robust advantage calculation. This enables agents to identify and prioritize crucial interactions with influential neighbors, thus enhancing the targeted coordination and collaboration among agents. Through comprehensive evaluations against state-of-the-art traffic signal control methods over three real-world traffic datasets composed of up to 196 intersections, we empirically show that CoordLight consistently exhibits superior performance across diverse traffic networks with varying traffic flows. The code is available at https://github.com/marmotlab/CoordLight

Related papers

• Robust Single-Agent Reinforcement Learning for Regional Traffic Signal Control Under Demand Fluctuations [5.784337914162491]
  Traffic congestion, primarily driven by intersection queuing, significantly impacts urban living standards, safety, environmental quality, and economic efficiency.<n>This study introduces a novel single-agent reinforcement learning framework for regional adaptive TSC.<n>The framework exhibits robust anti-fluctuation capability and significantly reduces queue lengths.
  arXiv  Detail & Related papers  (2025-11-01T13:18:50Z)
• Joint Optimization of Cooperation Efficiency and Communication Covertness for Target Detection with AUVs [105.81167650318054]
  This paper investigates underwater cooperative target detection using autonomous underwater vehicles (AUVs)<n>We first formulate a joint trajectory and power control optimization problem, and then present an innovative hierarchical action management framework to solve it.<n>Under the centralized training and decentralized execution paradigm, our target detection framework enables adaptive covert cooperation while satisfying both energy and mobility constraints.
  arXiv  Detail & Related papers  (2025-10-21T02:14:11Z)
• CoLLMLight: Cooperative Large Language Model Agents for Network-Wide Traffic Signal Control [7.0964925117958515]
  Traffic Signal Control (TSC) plays a critical role in urban traffic management by optimizing traffic flow and mitigating congestion.<n>Existing approaches fail to address the essential need for inter-agent coordination.<n>We propose CoLLMLight, a cooperative LLM agent framework for TSC.
  arXiv  Detail & Related papers  (2025-03-14T15:40:39Z)
• Unicorn: A Universal and Collaborative Reinforcement Learning Approach Towards Generalizable Network-Wide Traffic Signal Control [13.106167353085878]
  Adaptive traffic signal control (ATSC) is crucial in reducing congestion, maximizing throughput, and improving mobility in rapidly growing urban areas.<n>Recent advancements in parameter-sharing multi-agent reinforcement learning (MARL) have greatly enhanced the scalable and adaptive optimization of complex, dynamic flows in large-scale homogeneous networks.<n>We present Unicorn, a universal and collaborative MARL framework designed for efficient and adaptable network-wide ATSC.
  arXiv  Detail & Related papers  (2025-03-14T15:13:42Z)
• Reinforcement Learning for Adaptive Traffic Signal Control: Turn-Based and Time-Based Approaches to Reduce Congestion [2.733700237741334]
  This paper explores the use of Reinforcement Learning to enhance traffic signal operations at intersections. We introduce two RL-based algorithms: a turn-based agent, which dynamically prioritizes traffic signals based on real-time queue lengths, and a time-based agent, which adjusts signal phase durations according to traffic conditions. Simulation results demonstrate that both RL algorithms significantly outperform conventional traffic signal control systems.
  arXiv  Detail & Related papers  (2024-08-28T12:35:56Z)
• AgentsCoMerge: Large Language Model Empowered Collaborative Decision Making for Ramp Merging [46.69777653051523]
  Ramp merging is one of the bottlenecks in traffic systems, which commonly cause traffic congestion, accidents, and severe carbon emissions.<n>We propose a novel collaborative decision-making framework, named AgentsCoMerge, to leverage large language models (LLMs)
  arXiv  Detail & Related papers  (2024-08-07T08:34:48Z)
• Agent-Agnostic Centralized Training for Decentralized Multi-Agent Cooperative Driving [17.659812774579756]
  We propose an asymmetric actor-critic model that learns decentralized cooperative driving policies for autonomous vehicles. By employing attention neural networks with masking, our approach efficiently manages real-world traffic dynamics and partial observability.
  arXiv  Detail & Related papers  (2024-03-18T16:13:02Z)
• A Holistic Framework Towards Vision-based Traffic Signal Control with Microscopic Simulation [53.39174966020085]
  Traffic signal control (TSC) is crucial for reducing traffic congestion that leads to smoother traffic flow, reduced idling time, and mitigated CO2 emissions. In this study, we explore the computer vision approach for TSC that modulates on-road traffic flows through visual observation. We introduce a holistic traffic simulation framework called TrafficDojo towards vision-based TSC and its benchmarking.
  arXiv  Detail & Related papers  (2024-03-11T16:42:29Z)
• Safe Model-Based Multi-Agent Mean-Field Reinforcement Learning [48.667697255912614]
  Mean-field reinforcement learning addresses the policy of a representative agent interacting with the infinite population of identical agents. We propose Safe-M$3$-UCRL, the first model-based mean-field reinforcement learning algorithm that attains safe policies even in the case of unknown transitions. Our algorithm effectively meets the demand in critical areas while ensuring service accessibility in regions with low demand.
  arXiv  Detail & Related papers  (2023-06-29T15:57:07Z)
• SocialLight: Distributed Cooperation Learning towards Network-Wide Traffic Signal Control [7.387226437589183]
  SocialLight is a new multi-agent reinforcement learning method for traffic signal control. It learns cooperative traffic control policies by estimating the individual marginal contribution of agents on their local neighborhood. We benchmark our trained network against state-of-the-art traffic signal control methods on standard benchmarks in two traffic simulators.
  arXiv  Detail & Related papers  (2023-04-20T12:41:25Z)
• End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning [63.56464608571663]
  Navigating through intersections is one of the main challenging tasks for an autonomous vehicle. In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided. We propose a multi-agent system using a continuous, model-free Deep Reinforcement Learning algorithm used to train a neural network for predicting both the acceleration and the steering angle at each time step.
  arXiv  Detail & Related papers  (2021-04-28T07:54:40Z)
• MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control [54.162449208797334]
  Traffic signal control aims to coordinate traffic signals across intersections to improve the traffic efficiency of a district or a city. Deep reinforcement learning (RL) has been applied to traffic signal control recently and demonstrated promising performance where each traffic signal is regarded as an agent. We propose a novel Meta Variationally Intrinsic Motivated (MetaVIM) RL method to learn the decentralized policy for each intersection that considers neighbor information in a latent way.
  arXiv  Detail & Related papers  (2021-01-04T03:06:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.