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Applying reinforcement learning (RL) to combinatorial optimization problems is attractive as it removes the need for expert knowledge or pre-solved instances.
“Neural” computation of decisions in optimization problems
J. J. Hopfield and W. D. Tank · 1985
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
R. J. Williams · 1992
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Concorde TSP solver, 2006
D. Applegate, R. Bixby, V. Chvatal, and W. Cook · 2006
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Combinatorial optimization and green logistics
A. Sbihi and R. W. Eglese · 2007
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A hybrid genetic algorithm for multidepot and periodic vehicle routing problems
T. Vidal, T. G. Crainic, M. Gendreau, N. Lahrichi, and W. Rei · 2012
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Adam: A Method for Stochastic Optimization
D. P. Kingma and J. Ba · 2015
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Human-level control through deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. A. Riedmiller, A. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis · 2015
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Illuminating search spaces by mapping elites
J.-B. Mouret and J. Clune · 2015
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Pointer Networks
O. Vinyals, M. Fortunato, and N. Jaitly · 2015
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Quality Diversity: A New Frontier for Evolutionary Computation
J. K. Pugh, L. B. Soros, and K. O. Stanley · 2016
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WaveNet: A Generative Model for Raw Audio
A. van den Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, and K. Kavukcuoglu · 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
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. Gomez, L. Kaiser, and I. Polosukhin · 2017
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Quality and Diversity Optimization: A Unifying Modular Framework
A. Cully and Y. Demiris · 2018
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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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Meta-Reinforcement Learning of Structured Exploration Strategies
A. Gupta, R. Mendonca, Y. Liu, P. Abbeel, and S. Levine · 2018
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Diversity-Driven Exploration Strategy for Deep Reinforcement Learning
Z.-W. Hong, T.-Y. Shann, S.-Y. Su, Y.-H. Chang, and C.-Y. Lee · 2018
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Evolution-Guided Policy Gradient in Reinforcement Learning
S. Khadka and K. Tumer · 2018
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Reinforcement Learning for Solving the Vehicle Routing Problem
M. Nazari, A. Oroojlooy, L. V. Snyder, and M. Takáč · 2018
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A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel, T. Lillicrap, K. Simonyan, and D. Hassabis · 2018
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Reinforcement Learning: An Introduction
R. S. Sutton and A. G. Barto · 2018
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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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An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem
Skew-Fit: State-Covering Self-Supervised Reinforcement Learning
V. H. Pong, M. Dalal, S. Lin, A. Nair, S. Bahl, and S. Levine · 2020
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Refuting conjectures in extremal combinatorics via linear programming
A. Z. Wagner · 2020
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Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning
C. Zhang, W. Song, Z. Cao, J. Zhang, P. S. Tan, and C. Xu · 2020
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Machine learning for combinatorial optimization: A methodological tour d’horizon
Y. Bengio, A. Lodi, and A. Prouvost · 2021
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Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances
Z. Fu, K. Qiu, and H. Zha · 2021
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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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C. K. Joshi, T. Laurent, and X. Bresson · 2019
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Collaborative Evolutionary Reinforcement Learning
S. Khadka, S. Majumdar, T. Nassar, Z. Dwiel, E. Tumer, S. Miret, Y. Liu, and K. Tumer · 2019
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Attention, Learn to Solve Routing Problems!
W. Kool, H. van Hoof, and M. Welling · 2019
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OR-Tools, 2019
L. Perron and V. Furnon · 2019
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CEM-RL: Combining evolutionary and gradient-based methods for policy search
A. Pourchot and O. Sigaud · 2019
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Language Models are Few-Shot Learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, and C. Hesse · 2020
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Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement Learning
P. R. de O. da Costa, J. Rhuggenaath, Y. Zhang, and A. Akcay · 2020
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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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Deep Policy Dynamic Programming for Vehicle Routing Problems
W. Kool, H. van Hoof, J. Gromicho, and M. Welling · 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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Learning Improvement Heuristics for Solving Routing Problems
Y. Wu, W. Song, Z. Cao, J. Zhang, and A. Lim · 2021
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Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing Problems
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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Fast Population-Based Reinforcement Learning on a Single Machine
A. Flajolet, C. B. Monroc, K. Beguir, and T. Pierrot · 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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Diversity Policy Gradient for Sample Efficient Quality-Diversity Optimization
T. Pierrot, V. Macé, F. Chalumeau, A. Flajolet, G. Cideron, K. Beguir, A. Cully, O. Sigaud, and N. Perrin-Gilbert · 2022
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Hybrid genetic search for the cvrp: Open-source implementation and swap* neighborhood
T. Vidal · 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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DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems
R. Qiu, Z. Sun, and Y. Yang · 2023
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