Federated Hierarchical Reinforcement Learning for Adaptive Traffic Signal Control

• URL: http://arxiv.org/abs/2504.05553v1
• Date: Mon, 07 Apr 2025 23:02:59 GMT
• Title: Federated Hierarchical Reinforcement Learning for Adaptive Traffic Signal Control
• Authors: Yongjie Fu, Lingyun Zhong, Zifan Li, Xuan Di,
• Abstract summary: Multi-agent reinforcement learning (MARL) has shown promise for adaptive traffic signal control (ATSC)<n>MARL faces constraints due to extensive data sharing and communication requirements.<n>We propose Hierarchical Federated Reinforcement Learning (HFRL) for ATSC.
• Score: 5.570882985800125
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-agent reinforcement learning (MARL) has shown promise for adaptive traffic signal control (ATSC), enabling multiple intersections to coordinate signal timings in real time. However, in large-scale settings, MARL faces constraints due to extensive data sharing and communication requirements. Federated learning (FL) mitigates these challenges by training shared models without directly exchanging raw data, yet traditional FL methods such as FedAvg struggle with highly heterogeneous intersections. Different intersections exhibit varying traffic patterns, demands, and road structures, so performing FedAvg across all agents is inefficient. To address this gap, we propose Hierarchical Federated Reinforcement Learning (HFRL) for ATSC. HFRL employs clustering-based or optimization-based techniques to dynamically group intersections and perform FedAvg independently within groups of intersections with similar characteristics, enabling more effective coordination and scalability than standard FedAvg. Our experiments on synthetic and real-world traffic networks demonstrate that HFRL not only outperforms both decentralized and standard federated RL approaches but also identifies suitable grouping patterns based on network structure or traffic demand, resulting in a more robust framework for distributed, heterogeneous systems.

Related papers

• 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)
• Toward Dependency Dynamics in Multi-Agent Reinforcement Learning for Traffic Signal Control [8.312659530314937]
  Reinforcement learning (RL) emerges as a promising data-driven approach for adaptive traffic signal control.<n>In this paper, we propose a novel Dynamic Reinforcement Update Strategy for Deep Q-Network (DQN-DPUS)<n>We show that the proposed strategy can speed up the convergence rate without sacrificing optimal exploration.
  arXiv  Detail & Related papers  (2025-02-23T15:29:12Z)
• Heterogeneous Multi-Agent Reinforcement Learning for Distributed Channel Access in WLANs [47.600901884970845]
  This paper investigates the use of multi-agent reinforcement learning (MARL) to address distributed channel access in wireless local area networks.<n>In particular, we consider the challenging yet more practical case where the agents heterogeneously adopt value-based or policy-based reinforcement learning algorithms to train the model.<n>We propose a heterogeneous MARL training framework, named QPMIX, which adopts a centralized training with distributed execution paradigm to enable heterogeneous agents to collaborate.
  arXiv  Detail & Related papers  (2024-12-18T13:50:31Z)
• Improving Traffic Flow Predictions with SGCN-LSTM: A Hybrid Model for Spatial and Temporal Dependencies [55.2480439325792]
  This paper introduces the Signal-Enhanced Graph Convolutional Network Long Short Term Memory (SGCN-LSTM) model for predicting traffic speeds across road networks. Experiments on the PEMS-BAY road network traffic dataset demonstrate the SGCN-LSTM model's effectiveness.
  arXiv  Detail & Related papers  (2024-11-01T00:37:00Z)
• Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates [71.81037644563217]
  Synchronous federated learning (FL) is a popular paradigm for collaborative edge learning. As some of the devices may have limited computational resources and varying availability, FL latency is highly sensitive to stragglers. We propose straggler-aware layer-wise federated learning (SALF) that leverages the optimization procedure of NNs via backpropagation to update the global model in a layer-wise fashion.
  arXiv  Detail & Related papers  (2024-03-27T09:14:36Z)
• Adaptive Hierarchical SpatioTemporal Network for Traffic Forecasting [70.66710698485745]
  We propose an Adaptive Hierarchical SpatioTemporal Network (AHSTN) to promote traffic forecasting. AHSTN exploits the spatial hierarchy and modeling multi-scale spatial correlations. Experiments on two real-world datasets show that AHSTN achieves better performance over several strong baselines.
  arXiv  Detail & Related papers  (2023-06-15T14:50:27Z)
• 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)
• IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control [4.273991039651846]
  Scaling adaptive traffic-signal control involves dealing with state and action spaces. We introduce Inductive Graph Reinforcement Learning (IG-RL) based on graph-convolutional networks. Our model can generalize to new road networks, traffic distributions, and traffic regimes.
  arXiv  Detail & Related papers  (2020-03-06T17:17:59Z)

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.