The aim of the study is to provide interesting insights on how efficient machine learning
algorithms could be adapted to solve combinatorial optimization problems in conjunction …

This paper presents a framework to tackle combinatorial optimization problems using neural
networks and reinforcement learning. We focus on the traveling salesman problem (TSP) …

Abstract We address the Traveling Salesman Problem (TSP), a famous NP-hard
combinatorial optimization problem. And we propose a variable strategy reinforced …

We propose UTSP, an Unsupervised Learning (UL) framework for solving the Travelling
Salesman Problem (TSP). We train a Graph Neural Network (GNN) using a surrogate loss …

Recently deep reinforcement learning has shown success in solving NP-hard combinatorial
optimization problems such as traveling salesman problems, vehicle routing problems, job …

We introduce an experimentation procedure for evaluating and comparing optimization
algorithms based on the Traveling Salesman Problem (TSP). We argue that end-of-run …

Various neural network models have been proposed to tackle combinatorial optimization
problems such as the travelling salesman problem (TSP). Existing learning-based TSP …

Recent works using deep learning to solve the Traveling Salesman Problem (TSP) have
focused on learning construction heuristics. Such approaches find TSP solutions of good …

In this work, we introduce Graph Pointer Networks (GPNs) trained using reinforcement
learning (RL) for tackling the traveling salesman problem (TSP). GPNs build upon Pointer …

We propose an end-to-end learning framework based on hierarchical reinforcement
learning, called H-TSP, for addressing the large-scale Traveling Salesman Problem (TSP) …