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Deep reinforcement learning (RL) has proved to be a competitive heuristic for solving small-sized instances of traveling salesman problems (TSP), but its performance on larger-sized instances is insufficient.
The euclidean travelling salesman problem is np-complete
Christos H. Papadimitriou · 1977
Earlier work this paper cites.
TSPLIB–a traveling salesman problem library
Gerhard Reinelt · 1991
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams · 1992
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Convolutional networks for images, speech, and time-series
Y. Bengio and Yann Lecun · 1997
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Reinforcement learning: An introduction
R.S. Sutton and A.G. Barto · 1998
Earlier work this paper cites.
Algorithms for VLSI Design Automation , chapter Routing
Sabih H. Gerez · 1999
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An Introduction to Bioinformatics Algorithms
Neil C. Jones and Pavel A. Pevzner · 2004
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Concorde tsp solver
David Applegate, Bixby Ribert, Chvatal Vasek, and Cook William · 2004
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Deep symmetry networks
Robert Gens and Pedro M Domingos · 2014
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Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly · 2015
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An extension of the lin-kernighan-helsgaun tsp solver for constrained traveling salesman and vehicle routing problems
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Chaitanya K. Joshi, Thomas Laurent, and Xavier Bresson
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Learning 2-opt heuristics for the traveling salesman problem via deep reinforcement learning
Paulo R de O Da Costa, Jason Rhuggenaath, Yingqian Zhang, and Alp Akcay · 2020
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Learning 2-opt heuristics for the traveling salesman problem via deep reinforcement learning, 2020
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