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Recently numerous machine learning based methods for combinatorial optimization problems have been proposed that learn to construct solutions in a sequential decision process via reinforcement learning.
Neural Networks and Physical Systems with Emergent Collective Computational Abilities
John J. Hopfield · 1982
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Concorde TSP solver, 2006
David Applegate, Ribert Bixby, Vasek Chvatal, and William Cook · 2006
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Adam: A method for stochastic optimization
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Thibaut Vidal, Teodor Gabriel Crainic, Michel Gendreau, and Christian Prins · 2014
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Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly · 2015
Earlier work this paper cites.
Neural Combinatorial Optimization with Reinforcement Learning
Irwan Bello, Hieu Pham, Quoc V Le, Mohammad Norouzi, and Samy Bengio · 2016
Earlier work this paper cites.
An extension of the Lin-Kernighan-Helsgaun TSP solver for constrained traveling salesman and vehicle routing problems
Keld Helsgaun · 2017
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Learning combinatorial optimization algorithms over graphs
Elias Khalil, Hanjun Dai, Yuyu Zhang, Bistra Dilkina, and Le Song · 2017
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New benchmark instances for the capacitated vehicle routing problem
Eduardo Uchoa, Diego Pecin, Artur Pessoa, Marcus Poggi, Thibaut Vidal, and Anand Subramanian · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Learning heuristics for the TSP by policy gradient
Michel Deudon, Pierre Cournut, Alexandre Lacoste, Yossiri Adulyasak, and Louis-Martin Rousseau · 2018
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Combinatorial optimization with graph convolutional networks and guided tree search
Zhuwen Li, Qifeng Chen, and Vladlen Koltun · 2018
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Reinforcement learning for solving the vehicle routing problem
Mohammadreza Nazari, Afshin Oroojlooy, Lawrence Snyder, and Martin Takác · 2018
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Learning to perform local rewriting for combinatorial optimization
Xinyun Chen and Yuandong Tian · 2019
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An efficient graph convolutional network technique for the travelling salesman problem
scikit-optimize/scikit-optimize, September 2020
Tim Head, Manoj Kumar, Holger Nahrstaedt, Gilles Louppe, and Iaroslav Shcherbatyi · 2020
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Neural large neighborhood search for the capacitated vehicle routing problem
André Hottung and Kevin Tierney · 2020
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Deep learning assisted heuristic tree search for the container pre-marshalling problem
André Hottung, Shunji Tanaka, and Kevin Tierney · 2020
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POMO: policy optimization with multiple optima for reinforcement learning
Yeong-Dae Kwon, Jinho Choo, Byoungjip Kim, Iljoo Yoon, Youngjune Gwon, and Seungjai Min · 2020
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Learning combinatorial optimization on graphs: A survey with applications to networking
Natalia Vesselinova, Rebecca Steinert, Daniel F. Perez-Ramirez, and Magnus Boman · 2020
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Chaitanya K Joshi, Thomas Laurent, and Xavier Bresson · 2019
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Attention, learn to solve routing problems!
Wouter Kool, Herke van Hoof, and Max Welling · 2019
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A deep reinforcement learning algorithm using dynamic attention model for vehicle routing problems
Bo Peng, Jiahai Wang, and Zizhen Zhang · 2019
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Paulo R de O. da Costa, Jason Rhuggenaath, Yingqian Zhang, and Alp Akcay · 2020
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Jonas K Falkner and Lars Schmidt-Thieme · 2020
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Generalize a small pre-trained model to arbitrarily large TSP instances
Zhang-Hua Fu, Kai-Bin Qiu, and Hongyuan Zha · 2020
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OR-Tools
Laurent Perron and Vincent Furnon
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Cong Zhang, Wen Song, Zhiguang Cao, Jie Zhang, Puay Siew Tan, and Xu Chi · 2020
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Learning a latent search space for routing problems using variational autoencoders
André Hottung, Bhanu Bhandari, and Kevin Tierney · 2021
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Deep policy dynamic programming for vehicle routing problems
Wouter Kool, Herke van Hoof, Joaquim Gromicho, and Max Welling · 2021
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Learning improvement heuristics for solving routing problems
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Step-wise deep learning models for solving routing problems
Liang Xin, Wen Song, Zhiguang Cao, and Jie Zhang · 2021
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