Neural Combinatorial Optimization with Reinforcement Learning

by   Irwan Bello, et al.

This paper presents a framework to tackle combinatorial optimization problems using neural networks and reinforcement learning. We focus on the traveling salesman problem (TSP) and train a recurrent network that, given a set of city coordinates, predicts a distribution over different city permutations. Using negative tour length as the reward signal, we optimize the parameters of the recurrent network using a policy gradient method. We compare learning the network parameters on a set of training graphs against learning them on individual test graphs. Despite the computational expense, without much engineering and heuristic designing, Neural Combinatorial Optimization achieves close to optimal results on 2D Euclidean graphs with up to 100 nodes. Applied to the KnapSack, another NP-hard problem, the same method obtains optimal solutions for instances with up to 200 items.


page 1

page 2

page 3

page 4


A Differentiable Approach to Combinatorial Optimization using Dataless Neural Networks

The success of machine learning solutions for reasoning about discrete s...

Neural Combinatorial Optimization: a New Player in the Field

Neural Combinatorial Optimization attempts to learn good heuristics for ...

Exact-K Recommendation via Maximal Clique Optimization

This paper targets to a novel but practical recommendation problem named...

Feeder Load Balancing using Neural Network

The distribution system problems, such as planning, loss minimization, a...

Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement Learning

In this work, we introduce Graph Pointer Networks (GPNs) trained using r...

Deep Learning Chromatic and Clique Numbers of Graphs

Deep neural networks have been applied to a wide range of problems acros...

Annealed Training for Combinatorial Optimization on Graphs

The hardness of combinatorial optimization (CO) problems hinders collect...

Code Repositories



view repo

Please sign up or login with your details

Forgot password? Click here to reset