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

02/09/2020
by   Bo Peng, et al.
60

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/13/2022

Solving Dynamic Graph Problems with Multi-Attention Deep Reinforcement Learning

Graph problems such as traveling salesman problem, or finding minimal St...
research
08/27/2023

Towards Generalizable Neural Solvers for Vehicle Routing Problems via Ensemble with Transferrable Local Policy

Machine learning has been adapted to help solve NP-hard combinatorial op...
research
10/06/2021

Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer

Recently, Transformer has become a prevailing deep architecture for solv...
research
12/22/2021

A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with Drone

Reinforcement learning has recently shown promise in learning quality so...
research
02/13/2020

MODRL/D-AM: Multiobjective Deep Reinforcement Learning Algorithm Using Decomposition and Attention Model for Multiobjective Optimization

Recently, a deep reinforcement learning method is proposed to solve mult...
research
09/10/2021

Boosting Graph Search with Attention Network for Solving the General Orienteering Problem

Recently, several studies have explored the use of neural network to sol...
research
07/14/2022

Attention, Filling in The Gaps for Generalization in Routing Problems

Machine Learning (ML) methods have become a useful tool for tackling veh...

Please sign up or login with your details

Forgot password? Click here to reset