Solving the single-track train scheduling problem via Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2009.00433v2
• Date: Tue, 13 Jun 2023 08:01:42 GMT
• Title: Solving the single-track train scheduling problem via Deep Reinforcement Learning
• Abstract summary: Induced delays propagate through the network, leading to a mismatch in demand and offer for goods and passengers. It is the duty of human traffic controllers, the so-called dispatchers, to do their best to minimize the impact on traffic. This paper investigates Machine Learning-based methods for tackling this problem, proposing two different Deep Q-Learning methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Every day, railways experience disturbances and disruptions, both on the network and the fleet side, that affect the stability of rail traffic. Induced delays propagate through the network, which leads to a mismatch in demand and offer for goods and passengers, and, in turn, to a loss in service quality. In these cases, it is the duty of human traffic controllers, the so-called dispatchers, to do their best to minimize the impact on traffic. However, dispatchers inevitably have a limited depth of perception of the knock-on effect of their decisions, particularly how they affect areas of the network that are outside their direct control. In recent years, much work in Decision Science has been devoted to developing methods to solve the problem automatically and support the dispatchers in this challenging task. This paper investigates Machine Learning-based methods for tackling this problem, proposing two different Deep Q-Learning methods(Decentralized and Centralized). Numerical results show the superiority of these techniques with respect to the classical linear Q-Learning based on matrices. Moreover, the Centralized approach is compared with a MILP formulation showing interesting results. The experiments are inspired by data provided by a U.S. Class 1 railroad.

Related papers

• Towards Autonomous Railway Operations: A Semi-Hierarchical Deep Reinforcement Learning Approach to the Vehicle Rescheduling Problem [1.0348904561315861]
  Managing disruptions in railway traffic management is a major challenge.<n>Rising traffic density and infrastructure limits increase complexity.<n>Most dispatching still relies on human expertise due to the problem's exponential complexity.<n>This paper introduces a semi-hierarchical RL formulation tailored to operational constraints.
  arXiv  Detail & Related papers  (2026-05-11T09:23:50Z)
• Learning from Diverse Reasoning Paths with Routing and Collaboration [65.77515749498575]
  We propose Quality-filtered Routing with Cooperative Distillation (QR-Distill), combining path quality filtering, conditional routing, and peer teaching.<n> Experiments demonstrate QR-Distill's superiority over traditional single- and multi-path distillation methods.
  arXiv  Detail & Related papers  (2025-08-23T01:15:57Z)
• Reinforcement Learning-based Sequential Route Recommendation for System-Optimal Traffic Assignment [8.598431584462944]
  We propose a learning-based framework that reformulates the static SO traffic assignment problem as a single-agent deep reinforcement learning task.<n>We develop an MSA-guided deep Q-learning algorithm that integrates the iterative structure of traditional traffic assignment methods into the RL training process.<n>Results show that the RL agent converges to the theoretical SO solution in the Braess network and achieves only a 0.35% deviation in the OW network.
  arXiv  Detail & Related papers  (2025-05-27T08:33:02Z)
• Deep Reinforcement Learning for Autonomous Vehicle Intersection Navigation [0.24578723416255746]
  Reinforcement learning algorithms have emerged as a promising approach to address these challenges. Here, we address the problem of efficiently and safely navigating T-intersections using a lower-cost, single-agent approach. Our results reveal that the proposed approach enables the AV to effectively navigate T-intersections, outperforming previous methods in terms of travel delays, collision minimization, and overall cost.
  arXiv  Detail & Related papers  (2023-09-30T10:54:02Z)
• Using Reinforcement Learning for the Three-Dimensional Loading Capacitated Vehicle Routing Problem [40.50169360761464]
  Collaborative vehicle routing has been proposed as a solution to increase efficiency. Current operations research methods suffer from non-linear scaling with increasing problem size. We develop a reinforcement learning model to solve the three-dimensional loading capacitated vehicle routing problem in approximately linear time.
  arXiv  Detail & Related papers  (2023-07-22T18:05:28Z)
• Rethinking Closed-loop Training for Autonomous Driving [82.61418945804544]
  We present the first empirical study which analyzes the effects of different training benchmark designs on the success of learning agents. We propose trajectory value learning (TRAVL), an RL-based driving agent that performs planning with multistep look-ahead. Our experiments show that TRAVL can learn much faster and produce safer maneuvers compared to all the baselines.
  arXiv  Detail & Related papers  (2023-06-27T17:58:39Z)
• Detecting train driveshaft damages using accelerometer signals and Differential Convolutional Neural Networks [67.60224656603823]
  This paper proposes the development of a railway axle condition monitoring system based on advanced 2D-Convolutional Neural Network (CNN) architectures. The resultant system converts the railway axle vibration signals into time-frequency domain representations, i.e., spectrograms, and, thus, trains a two-dimensional CNN to classify them depending on their cracks.
  arXiv  Detail & Related papers  (2022-11-15T15:04:06Z)
• On partitioning of an SHM problem and parallels with transfer learning [0.0]
  A problem-splitting approach and a scheme motivated by transfer learning is applied to a structural health monitoring problem. The specific problem in this case is that of localising damage on an aircraft wing.
  arXiv  Detail & Related papers  (2022-03-03T11:22:30Z)
• To Supervise or Not: How to Effectively Learn Wireless Interference Management Models? [43.317056712257994]
  Machine learning has become successful in solving wireless interference management problems. There are two popular training paradigms for such interference management models: supervised learning and unsupervised learning. We show that for some special power control problem, the unsupervised learning can perform much worse than its supervised counterpart.
  arXiv  Detail & Related papers  (2021-12-28T06:29:08Z)
• Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulation [54.31414116478024]
  In mobile manipulation (MM), robots can both navigate within and interact with their environment. In this work, we explore how to apply imitation learning (IL) to learn continuous visuo-motor policies for MM tasks.
  arXiv  Detail & Related papers  (2021-12-09T23:54:59Z)
• A Deep Value-network Based Approach for Multi-Driver Order Dispatching [55.36656442934531]
  We propose a deep reinforcement learning based solution for order dispatching. We conduct large scale online A/B tests on DiDi's ride-dispatching platform. Results show that CVNet consistently outperforms other recently proposed dispatching methods.
  arXiv  Detail & Related papers  (2021-06-08T16:27:04Z)
• Step-Ahead Error Feedback for Distributed Training with Compressed Gradient [99.42912552638168]
  We show that a new "gradient mismatch" problem is raised by the local error feedback in centralized distributed training. We propose two novel techniques, 1) step ahead and 2) error averaging, with rigorous theoretical analysis.
  arXiv  Detail & Related papers  (2020-08-13T11:21:07Z)
• Tomography Based Learning for Load Distribution through Opaque Networks [9.923523030849836]
  Key task for over-the-top (OTT) service providers is sending traffic through the networks to minimize delays. We consider this problem in a general setting where traffic sources can choose a set of ingresses through which their traffic enter a black box network. Key technical challenges to solving this problem include the high dimensionality of the problem and handling constraints that are intrinsic to networks.
  arXiv  Detail & Related papers  (2020-07-18T21:52:21Z)
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks [84.2155885234293]
  We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
  arXiv  Detail & Related papers  (2020-02-22T14:38:11Z)

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.