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Learning Heuristics for the TSP by Policy Gradient

  • Michel Deudon
  • Pierre Cournut
  • Alexandre Lacoste
  • Yossiri Adulyasak
  • Louis-Martin Rousseau
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10848)

Abstract

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 with existing heuristic procedures. More specifically, we extend the neural combinatorial optimization framework to solve the traveling salesman problem (TSP). In this framework, the city coordinates are used as inputs and the neural network is trained using reinforcement learning to predict a distribution over city permutations. Our proposed framework differs from the one in [1] since we do not make use of the Long Short-Term Memory (LSTM) architecture and we opted to design our own critic to compute a baseline for the tour length which results in more efficient learning. More importantly, we further enhance the solution approach with the well-known 2-opt heuristic. The results show that the performance of the proposed framework alone is generally as good as high performance heuristics (OR-Tools). When the framework is equipped with a simple 2-opt procedure, it could outperform such heuristics and achieve close to optimal results on 2D Euclidean graphs. This demonstrates that our approach based on machine learning techniques could learn good heuristics which, once being enhanced with a simple local search, yield promising results.

Keywords

Combinatorial optimization Traveling salesman Policy gradient Neural networks Reinforcement learning 

Notes

Acknowledgment

We would like to thank Polytechnique Montreal and CIRRELT for financial and logistic support, Element AI for hosting weekly meetings as well as Compute Canada, Calcul Quebec and Telecom Paris-Tech for computational resources. We are also grateful to all the reviewers for their valuable and detailed feedback.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Michel Deudon
    • 1
  • Pierre Cournut
    • 1
  • Alexandre Lacoste
    • 2
  • Yossiri Adulyasak
    • 3
  • Louis-Martin Rousseau
    • 4
  1. 1.Polytechnique (France)PalaiseauFrance
  2. 2.Element AIMontrealCanada
  3. 3.HEC MontréalMontrealCanada
  4. 4.Polytechnique MontréalMontrealCanada

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