ModelLight: Model-Based Meta-Reinforcement Learning for Traffic Signal Control

• URL: http://arxiv.org/abs/2111.08067v1
• Date: Mon, 15 Nov 2021 20:25:08 GMT
• Title: ModelLight: Model-Based Meta-Reinforcement Learning for Traffic Signal Control
• Authors: Xingshuai Huang, Di Wu, Michael Jenkin and Benoit Boulet
• Abstract summary: This paper proposes a novel model-based meta-reinforcement learning framework (ModelLight) for traffic signal control. Within ModelLight, an ensemble of models for road intersections and the optimization-based meta-learning method are used to improve the data efficiency of an RL-based traffic light control method. Experiments on real-world datasets demonstrate that ModelLight can outperform state-of-the-art traffic light control algorithms.
• Score: 5.219291917441908
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traffic signal control is of critical importance for the effective use of transportation infrastructures. The rapid increase of vehicle traffic and changes in traffic patterns make traffic signal control more and more challenging. Reinforcement Learning (RL)-based algorithms have demonstrated their potential in dealing with traffic signal control. However, most existing solutions require a large amount of training data, which is unacceptable for many real-world scenarios. This paper proposes a novel model-based meta-reinforcement learning framework (ModelLight) for traffic signal control. Within ModelLight, an ensemble of models for road intersections and the optimization-based meta-learning method are used to improve the data efficiency of an RL-based traffic light control method. Experiments on real-world datasets demonstrate that ModelLight can outperform state-of-the-art traffic light control algorithms while substantially reducing the number of required interactions with the real-world environment.

Related papers

• MoveLight: Enhancing Traffic Signal Control through Movement-Centric Deep Reinforcement Learning [13.369840354712021]
  MoveLight is a novel traffic signal control system that enhances urban traffic management through movement-centric deep reinforcement learning. By leveraging detailed real-time data and advanced machine learning techniques, MoveLight overcomes the limitations of traditional traffic signal control methods.
  arXiv  Detail & Related papers  (2024-07-24T14:17:16Z)
• 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)
• Language-Guided Traffic Simulation via Scene-Level Diffusion [46.47977644226296]
  We present CTG++, a scene-level conditional diffusion model that can be guided by language instructions. We first propose a scene-level diffusion model equipped with atemporal backbone which generates realistic and controllable traffic. We then harness a large language model (LLM) to convert a users query into a loss function guiding the diffusion model towards query-compliant generation.
  arXiv  Detail & Related papers  (2023-06-10T05:20:30Z)
• Reinforcement Learning Approaches for Traffic Signal Control under Missing Data [5.896742981602458]
  In real-world urban scenarios, missing observation of traffic states may frequently occur due to the lack of sensors. We propose two solutions: the first one imputes the traffic states to enable adaptive control, and the second one imputes both states and rewards to enable adaptive control and the training of RL agents.
  arXiv  Detail & Related papers  (2023-04-21T03:26:33Z)
• Random Ensemble Reinforcement Learning for Traffic Signal Control [5.191217870404512]
  An efficient traffic signal control strategy can reduce traffic congestion, improve urban road traffic efficiency and facilitate people's lives. Existing reinforcement learning approaches for traffic signal control mainly focus on learning through a separate neural network.
  arXiv  Detail & Related papers  (2022-03-10T08:45:47Z)
• A Deep Reinforcement Learning Approach for Traffic Signal Control Optimization [14.455497228170646]
  Inefficient traffic signal control methods may cause numerous problems, such as traffic congestion and waste of energy. This paper first proposes a multi-agent deep deterministic policy gradient (MADDPG) method by extending the actor-critic policy gradient algorithms.
  arXiv  Detail & Related papers  (2021-07-13T14:11:04Z)
• 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)
• Multi-intersection Traffic Optimisation: A Benchmark Dataset and a Strong Baseline [85.9210953301628]
  Control of traffic signals is fundamental and critical to alleviate traffic congestion in urban areas. Because of the high complexity of modelling the problem, experimental settings of current works are often inconsistent. We propose a novel and strong baseline model based on deep reinforcement learning with the encoder-decoder structure.
  arXiv  Detail & Related papers  (2021-01-24T03:55:39Z)
• 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)
• Deep traffic light detection by overlaying synthetic context on arbitrary natural images [49.592798832978296]
  We propose a method to generate artificial traffic-related training data for deep traffic light detectors. This data is generated using basic non-realistic computer graphics to blend fake traffic scenes on top of arbitrary image backgrounds. It also tackles the intrinsic data imbalance problem in traffic light datasets, caused mainly by the low amount of samples of the yellow state.
  arXiv  Detail & Related papers  (2020-11-07T19:57:22Z)
• GeneraLight: Improving Environment Generalization of Traffic Signal Control via Meta Reinforcement Learning [35.351323110536924]
  We propose a novel traffic flow generator based on Wasserstein generative adversarial network to generate sufficient diverse and quality traffic flows. GeneraLight boosts the generalization performance by combining the idea of flow clustering and model-agnostic meta-learning.
  arXiv  Detail & Related papers  (2020-09-17T04:14:28Z)

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.