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Combinatorial Optimization underpins many real-world applications and yet, designing performant algorithms to solve these complex, typically NP-hard, problems remains a significant research challenge.
“neural” computation of decisions in optimization problems
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Concorde TSP solver, 2006
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A survey of monte carlo tree search methods
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Maintenance scheduling in the electricity industry: A literature review
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Neural combinatorial optimization with reinforcement learning
I. Bello, H. Pham, Q. V. Le, M. Norouzi, and S. Bengio · 2016
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Learning combinatorial optimization algorithms over graphs
H. Dai, E. B. Khalil, Y. Zhang, B. Dilkina, and L. Song · 2017
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An extension of the lin-kernighan-helsgaun tsp solver for constrained traveling salesman and vehicle routing problems
K. Helsgaun · 2017
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Attention is all you need
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Learning heuristics for the tsp by policy gradient
M. Deudon, P. Cournut, A. Lacoste, Y. Adulyasak, and L.-M. Rousseau · 2018
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Ranked reward: Enabling self-play reinforcement learning for combinatorial optimization
A. Laterre, Y. Fu, M. K. Jabri, A.-S. Cohen, D. Kas, K. Hajjar, T. S. Dahl, A. Kerkeni, and K. Beguir · 2018
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Evolving diverse tsp instances by means of novel and creative mutation operators
J. Bossek, P. Kerschke, A. Neumann, M. Wagner, F. Neumann, and H. Trautmann · 2019
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Learning to perform local rewriting for combinatorial optimization
X. Chen and Y. Tian · 2019
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Diversity is all you need: Learning skills without a reward function
B. Eysenbach, A. Gupta, J. Ibarz, and S. Levine · 2019
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Learning a latent search space for routing problems using variational autoencoders
A. Hottung, B. Bhandari, and K. Tierney · 2021
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Learning collaborative policies to solve np-hard routing problems
M. Kim, J. Park, and J. Kim · 2021
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Learning to iteratively solve routing problems with dual-aspect collaborative transformer
Y. Ma, J. Li, Z. Cao, W. Song, L. Zhang, Z. Chen, and J. Tang · 2021
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Reinforcement learning for combinatorial optimization: A survey
N. Mazyavkina, S. Sviridov, S. Ivanov, and E. Burnaev · 2021
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Multi-decoder attention model with embedding glimpse for solving vehicle routing problems
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Dynamics-aware unsupervised discovery of skills
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Exploratory combinatorial optimization with reinforcement learning
T. D. Barrett, W. R. Clements, J. N. Foerster, and A. I. Lvovsky · 2020
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Learning 2-opt heuristics for the traveling salesman problem via deep reinforcement learning
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L. Xin, W. Song, Z. Cao, and J. Zhang · 2021
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Simulation-guided beam search for neural combinatorial optimization
J. Choo, Y.-D. Kwon, J. Kim, J. Jae, A. Hottung, K. Tierney, and Y. Gwon · 2022
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Population-based reinforcement learning for combinatorial optimization
N. Grinsztajn, D. Furelos-Blanco, and T. D. Barrett · 2022
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Efficient active search for combinatorial optimization problems
A. Hottung, Y.-D. Kwon, and K. Tierney · 2022
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Sym-nco: Leveraging symmetricity for neural combinatorial optimization
M. Kim, J. Park, and J. Park · 2022
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Accelerated quality-diversity for robotics through massive parallelism
B. Lim, M. Allard, L. Grillotti, and A. Cully · 2022
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Learning improvement heuristics for solving routing problems
Y. Wu, W. Song, Z. Cao, J. Zhang, and A. Lim · 2022
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Jumanji: a suite of diverse and challenging reinforcement learning environments in jax, 2023
C. Bonnet, D. Luo, D. Byrne, S. Abramowitz, V. Coyette, P. Duckworth, D. Furelos-Blanco, N. Grinsztajn, T. Kalloniatis, V. Le, O. Mahjoub, L. Midgley, S. Surana, C. Waters, and A. Laterre · 2023
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Meta-SAGE: Scale meta-learning scheduled adaptation with guided exploration for mitigating scale shift on combinatorial optimization
J. Son, M. Kim, H. Kim, and J. Park · 2023
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