A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing Problems

• URL: http://arxiv.org/abs/2002.03282v1
• Date: Sun, 9 Feb 2020 04:51:53 GMT
• Title: A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing Problems
• Authors: Bo Peng and Jiahai Wang and Zizhen Zhang
• Abstract summary: This paper focuses on a challenging NP-hard problem, vehicle routing problem. Our model outperforms the previous methods and also shows a good generalization performance.
• Score: 20.52666896700441
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent researches show that machine learning has the potential to learn better heuristics than the one designed by human for solving combinatorial optimization problems. The deep neural network is used to characterize the input instance for constructing a feasible solution incrementally. Recently, an attention model is proposed to solve routing problems. In this model, the state of an instance is represented by node features that are fixed over time. However, the fact is, the state of an instance is changed according to the decision that the model made at different construction steps, and the node features should be updated correspondingly. Therefore, this paper presents a dynamic attention model with dynamic encoder-decoder architecture, which enables the model to explore node features dynamically and exploit hidden structure information effectively at different construction steps. This paper focuses on a challenging NP-hard problem, vehicle routing problem. The experiments indicate that our model outperforms the previous methods and also shows a good generalization performance.

Related papers

• AI-Aided Kalman Filters [65.35350122917914]
  The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing. Recent developments illustrate the possibility of fusing deep neural networks (DNNs) with classic Kalman-type filtering. This article provides a tutorial-style overview of design approaches for incorporating AI in aiding KF-type algorithms.
  arXiv  Detail & Related papers  (2024-10-16T06:47:53Z)
• GASE: Graph Attention Sampling with Edges Fusion for Solving Vehicle Routing Problems [6.084414764415137]
  We propose an adaptive Graph Attention Sampling with the Edges Fusion framework to solve vehicle routing problems. Our proposed model outperforms the existing methods by 2.08%-6.23% and shows stronger generalization ability.
  arXiv  Detail & Related papers  (2024-05-21T03:33:07Z)
• ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction [82.81767856234956]
  This paper proposes a new learning framework named ConCerNet to improve the trustworthiness of the DNN based dynamics modeling. We show that our method consistently outperforms the baseline neural networks in both coordinate error and conservation metrics.
  arXiv  Detail & Related papers  (2023-02-11T21:07:30Z)
• Combining Constructive and Perturbative Deep Learning Algorithms for the Capacitated Vehicle Routing Problem [0.0]
  The Capacitated Vehicle Routing Problem is a well-known NP-hard problem that poses the challenge of finding the optimal route of a vehicle delivering products to multiple locations. Recently, new efforts have emerged to create constructive and perturbative Deep Learning-based algorithms to tackle this problem. We propose a Combined Deep Constructor and Perturbator, which combines two powerful constructive and perturbative Deep Learning-based algorithms.
  arXiv  Detail & Related papers  (2022-11-25T06:20:11Z)
• Solving the vehicle routing problem with deep reinforcement learning [0.0]
  This paper focuses on the application of RL for the Vehicle Routing Problem (VRP), a famous problem that belongs to the class of NP-Hard problems. In a second phase, the neural architecture behind the Actor and Critic has been established, choosing to adopt a neural architecture based on the Convolutional neural networks. Experiments performed on a wide range of instances show that the algorithm has good generalization capabilities and can reach good solutions in a short time.
  arXiv  Detail & Related papers  (2022-07-30T12:34:26Z)
• Invariant Causal Mechanisms through Distribution Matching [86.07327840293894]
  In this work we provide a causal perspective and a new algorithm for learning invariant representations. Empirically we show that this algorithm works well on a diverse set of tasks and in particular we observe state-of-the-art performance on domain generalization.
  arXiv  Detail & Related papers  (2022-06-23T12:06:54Z)
• Constructing Neural Network-Based Models for Simulating Dynamical Systems [59.0861954179401]
  Data-driven modeling is an alternative paradigm that seeks to learn an approximation of the dynamics of a system using observations of the true system. This paper provides a survey of the different ways to construct models of dynamical systems using neural networks. In addition to the basic overview, we review the related literature and outline the most significant challenges from numerical simulations that this modeling paradigm must overcome.
  arXiv  Detail & Related papers  (2021-11-02T10:51:42Z)
• Redefining Neural Architecture Search of Heterogeneous Multi-Network Models by Characterizing Variation Operators and Model Components [71.03032589756434]
  We investigate the effect of different variation operators in a complex domain, that of multi-network heterogeneous neural models. We characterize both the variation operators, according to their effect on the complexity and performance of the model; and the models, relying on diverse metrics which estimate the quality of the different parts composing it.
  arXiv  Detail & Related papers  (2021-06-16T17:12:26Z)
• Causality-aware counterfactual confounding adjustment for feature representations learned by deep models [14.554818659491644]
  Causal modeling has been recognized as a potential solution to many challenging problems in machine learning (ML) We describe how a recently proposed counterfactual approach can still be used to deconfound the feature representations learned by deep neural network (DNN) models.
  arXiv  Detail & Related papers  (2020-04-20T17:37:36Z)
• Forecasting Sequential Data using Consistent Koopman Autoencoders [52.209416711500005]
  A new class of physics-based methods related to Koopman theory has been introduced, offering an alternative for processing nonlinear dynamical systems. We propose a novel Consistent Koopman Autoencoder model which, unlike the majority of existing work, leverages the forward and backward dynamics. Key to our approach is a new analysis which explores the interplay between consistent dynamics and their associated Koopman operators.
  arXiv  Detail & Related papers  (2020-03-04T18:24:30Z)
• MODRL/D-AM: Multiobjective Deep Reinforcement Learning Algorithm Using Decomposition and Attention Model for Multiobjective Optimization [15.235261981563523]
  This paper proposes a multiobjective deep reinforcement learning method to solve multiobjective optimization problem. In our method, each subproblem is solved by an attention model, which can exploit the structure features as well as node features of input nodes.
  arXiv  Detail & Related papers  (2020-02-13T12:59:39Z)

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.