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We propose ScheduleNet, a RL-based real-time scheduler, that can solve various types of multi-agent scheduling problems.
Probabilistic learning combinations of local job-shop scheduling rules
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A genetic algorithm applicable to large-scale job-shop problems
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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Distributed policy search reinforcement learning for job-shop scheduling tasks
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Tackling the bi-criteria facet of multiple traveling salesman problem with ant colony systems
R. Necula, M. Breaban, and M. Raschip · 2015
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D. Ha, A. Dai, and Q. V. Le · 2016
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Mastering the game of go with deep neural networks and tree search
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al · 2016
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Dynamic programming for routing and scheduling: Optimizing sequences of decisions
J. Van Hoorn · 2016
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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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Learning combinatorial optimization algorithms over graphs
E. Khalil, H. Dai, Y. Zhang, B. Dilkina, and L. Song · 2017
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Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
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Addressing function approximation error in actor-critic methods
Multiplicative interactions and where to find them
S. M. Jayakumar, W. M. Czarnecki, J. Menick, J. Schwarz, J. Rae, S. Osindero, Y. W. Teh, T. Harley, and R. Pascanu · 2019
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Smart manufacturing scheduling with edge computing using multi-class deep q network
C.-C. Lin, D.-J. Deng, Y.-L. Chih, and H.-T. Chiu · 2019
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Som-guided evolutionary search for solving minmax multiple-tsp
V.-I. Lupoaie, I.-A. Chili, M. E. Breaban, and M. Raschip · 2019
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Learning what to defer for maximum independent sets
S. Ahn, Y. Seo, and J. Shin · 2020
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Learning 2-opt heuristics for the traveling salesman problem via deep reinforcement learning
P. R. d. O. da Costa, J. Rhuggenaath, Y. Zhang, and A. Akcay · 2020
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A reinforcement learning approach for optimizing multiple traveling salesman problems over graphs
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S. Fujimoto, H. Van Hoof, and D. Meger · 2018
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Learning to search with mctsnets
A. Guez, T. Weber, I. Antonoglou, K. Simonyan, O. Vinyals, D. Wierstra, R. Munos, and D. Silver · 2018
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Averaging weights leads to wider optima and better generalization
P. Izmailov, D. Podoprikhin, T. Garipov, D. Vetrov, and A. G. Wilson · 2018
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Learning the multiple traveling salesmen problem with permutation invariant pooling networks
Y. Kaempfer and L. Wolf · 2018
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Attention, learn to solve routing problems!
W. Kool, H. Van Hoof, and M. Welling · 2018
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Reinforcement learning for solving the vehicle routing problem
M. Nazari, A. Oroojlooy, L. Snyder, and M. Takác · 2018
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Learning by playing solving sparse reward tasks from scratch
M. Riedmiller, R. Hafner, T. Lampe, M. Neunert, J. Degrave, T. Wiele, V. Mnih, N. Heess, and J. T. Springenberg · 2018
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Y. Hu, Y. Yao, and W. S. Lee · 2020
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Learning tsp requires rethinking generalization, 2020
C. K. Joshi, Q. Cappart, L.-M. Rousseau, T. Laurent, and X. Bresson · 2020
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A learning-based iterative method for solving vehicle routing problems
H. Lu, X. Zhang, and S. Yang · 2020
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Reinforcement learning for combinatorial optimization: A survey
N. Mazyavkina, S. Sviridov, S. Ivanov, and E. Burnaev · 2020
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Learning improvement heuristics for solving routing problems, 2020
Y. Wu, W. Song, Z. Cao, J. Zhang, and A. Lim · 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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Generalize a small pre-trained model to arbitrarily large tsp instances, 2021
Z.-H. Fu, K.-B. Qiu, and H. Zha · 2021
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Deep policy dynamic programming for vehicle routing problems, 2021
W. Kool, H. van Hoof, J. Gromicho, and M. Welling · 2021
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Learning to schedule job-shop problems: representation and policy learning using graph neural network and reinforcement learning
J. Park, J. Chun, S. H. Kim, Y. Kim, and J. Park · 2021
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