AMM: Adaptive Modularized Reinforcement Model for Multi-city Traffic Signal Control

• URL: http://arxiv.org/abs/2501.02548v1
• Date: Sun, 05 Jan 2025 13:59:08 GMT
• Title: AMM: Adaptive Modularized Reinforcement Model for Multi-city Traffic Signal Control
• Authors: Zherui Huang, Yicheng Liu, Chumeng Liang, Guanjie Zheng,
• Abstract summary: Traffic signal control (TSC) is an important and widely studied direction. Applying reinforcement learning (RL) methods to the real world is challenging due to the huge cost of experiments in real-world traffic environments. One possible solution is TSC domain adaptation, which adapts trained models to target environments.
• Score: 9.885854146962624
• License:
• Abstract: Traffic signal control (TSC) is an important and widely studied direction. Recently, reinforcement learning (RL) methods have been used to solve TSC problems and achieve superior performance over conventional TSC methods. However, applying RL methods to the real world is challenging due to the huge cost of experiments in real-world traffic environments. One possible solution is TSC domain adaptation, which adapts trained models to target environments and reduces the number of interactions and the training cost. However, existing TSC domain adaptation methods still face two major issues: the lack of consideration for differences across cities and the low utilization of multi-city data. To solve aforementioned issues, we propose an approach named Adaptive Modularized Model (AMM). By modularizing TSC problems and network models, we overcome the challenge of possible changes in environmental observations. We also aggregate multi-city experience through meta-learning. We conduct extensive experiments on different cities and show that AMM can achieve excellent performance with limited interactions in target environments and outperform existing methods. We also demonstrate the feasibility and generalizability of our method.

Related papers

• Meta-Federated Learning: A Novel Approach for Real-Time Traffic Flow Management [0.0]
  This paper introduces a novel approach by combining Federated Learning (FL) and Meta-Learning (ML) to create a decentralized, scalable, and adaptive traffic management system. We implement our model across a simulated network of smart traffic devices, demonstrating that Meta-Federated Learning significantly outperforms traditional models in terms of prediction accuracy and response time. Our approach shows remarkable adaptability to sudden changes in traffic patterns, suggesting a scalable solution for real-time traffic management in smart cities.
  arXiv  Detail & Related papers  (2025-01-28T07:24:24Z)
• Collaborative Imputation of Urban Time Series through Cross-city Meta-learning [54.438991949772145]
  We propose a novel collaborative imputation paradigm leveraging meta-learned implicit neural representations (INRs) We then introduce a cross-city collaborative learning scheme through model-agnostic meta learning. Experiments on a diverse urban dataset from 20 global cities demonstrate our model's superior imputation performance and generalizability.
  arXiv  Detail & Related papers  (2025-01-20T07:12:40Z)
• LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban Environments [3.7788636451616697]
  This work introduces an innovative approach that integrates Large Language Models into traffic signal control systems. A hybrid framework that augments LLMs with a suite of perception and decision-making tools is proposed. The findings from our simulations attest to the system's adeptness in adjusting to a multiplicity of traffic environments.
  arXiv  Detail & Related papers  (2024-03-13T08:41:55Z)
• Unleashing Network Potentials for Semantic Scene Completion [50.95486458217653]
  This paper proposes a novel SSC framework - Adrial Modality Modulation Network (AMMNet) AMMNet introduces two core modules: a cross-modal modulation enabling the interdependence of gradient flows between modalities, and a customized adversarial training scheme leveraging dynamic gradient competition. Extensive experimental results demonstrate that AMMNet outperforms state-of-the-art SSC methods by a large margin.
  arXiv  Detail & Related papers  (2024-03-12T11:48:49Z)
• Model-free Learning of Corridor Clearance: A Near-term Deployment Perspective [5.39179984304986]
  An emerging public health application of connected and automated vehicle (CAV) technologies is to reduce response times of emergency medical service (EMS) by indirectly coordinating traffic. Existing research on this topic often overlooks the impact of EMS vehicle disruptions on regular traffic, assumes 100% CAV penetration, relies on real-time traffic signal timing data and queue lengths at intersections, and makes various assumptions about traffic settings when deriving optimal model-based CAV control strategies. To overcome these challenges and enhance real-world applicability in near-term, we propose a model-free approach employing deep reinforcement learning (DRL) for designing CAV control strategies
  arXiv  Detail & Related papers  (2023-12-16T06:08:53Z)
• MARLIN: Soft Actor-Critic based Reinforcement Learning for Congestion Control in Real Networks [63.24965775030673]
  We propose a novel Reinforcement Learning (RL) approach to design generic Congestion Control (CC) algorithms. Our solution, MARLIN, uses the Soft Actor-Critic algorithm to maximize both entropy and return. We trained MARLIN on a real network with varying background traffic patterns to overcome the sim-to-real mismatch.
  arXiv  Detail & Related papers  (2023-02-02T18:27:20Z)
• Cycle-Consistent World Models for Domain Independent Latent Imagination [0.0]
  High costs and risks make it hard to train autonomous cars in the real world. We propose a novel model-based reinforcement learning approach called Cycleconsistent World Models.
  arXiv  Detail & Related papers  (2021-10-02T13:55:50Z)
• Reinforcement Learning for Adaptive Mesh Refinement [63.7867809197671]
  We propose a novel formulation of AMR as a Markov decision process and apply deep reinforcement learning to train refinement policies directly from simulation. The model sizes of these policy architectures are independent of the mesh size and hence scale to arbitrarily large and complex simulations.
  arXiv  Detail & Related papers  (2021-03-01T22:55:48Z)
• 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)
• A Decentralized Policy Gradient Approach to Multi-task Reinforcement Learning [13.733491423871383]
  We develop a framework for solving multi-task reinforcement learning problems. The goal is to learn a common policy that operates effectively in different environments. We highlight two fundamental challenges in MTRL that are not present in its single task counterpart.
  arXiv  Detail & Related papers  (2020-06-08T03:28:19Z)
• GACEM: Generalized Autoregressive Cross Entropy Method for Multi-Modal Black Box Constraint Satisfaction [69.94831587339539]
  We present a modified Cross-Entropy Method (CEM) that uses a masked auto-regressive neural network for modeling uniform distributions over the solution space. Our algorithm is able to express complicated solution spaces, thus allowing it to track a variety of different solution regions.
  arXiv  Detail & Related papers  (2020-02-17T20:21:20Z)

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.