1 |
AUSIELLO G, CRESCENZI P, GAMBBOSI G, et al. Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties[M]. Berlin: Springer, 1999: 21-37. 10.1007/978-3-642-58412-1_1
|
2 |
HOCHBA D S. Approximation algorithms for NP-hard problems [J]. ACM SIGACT News, 1997, 28(2):40-52. 10.1145/261342.571216
|
3 |
SUTTON R, BARTO A G. Reinforcement learning [J]. Neural Systems for Control, 1998, 15(7):665-685.
|
4 |
VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need [C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc., 2017:6000-6010. 10.1016/s0262-4079(17)32358-8
|
5 |
LIANG E, LIAW R, NISHIHARA R, et al. RLlib: abstractions for distributed reinforcement learning [C]// Proceedings of the 35th International Conference on Machine Learning. New York: PMLR.org, 2018:3053-3062.
|
6 |
KHALIL E, DAI H J, ZHANG Y Y, et al. Learning combinatorial optimization algorithms over graphs [C]// Proceedings of the 30th International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc., 2017:6351-6361.
|
7 |
VINYALS O, FORTUNATO M, JAITLY N. Pointer networks [C]// Proceedings of the 28th International Conference on Neural Information Processing Systems. Cambridge: MIT Press, 2015:2692-2700.
|
8 |
BELLO I, PHAM H, LE Q V, et al. Neural combinatorial optimization with reinforcement learning [C]// Proceedings of the 2017 International Conference on Learning Representations. Waterloo: University of Waterloo, 2017:1-15.
|
9 |
SUTSKEVER I, VINYALS O, LE Q V. Sequence to sequence learning with neural networks [C]// Proceedings of the 27th International Conference on Neural Information Processing Systems. Cambridge: MIT Press, 2014:3104-3112.
|
10 |
HAUSKNECHT M, STONE P. Deep recurrent Q-learning for partially observable MDPs [C]// Proceedings of the 2015 International Conference on the Association for the Advance of Artificial Intelligence. Palo Alto: AAAI Press, 2015:29-37.
|
11 |
KULKARNI T D, NARASIMHAN K R, SARRDI A, et al. Hierarchical deep reinforcement learning: integrating temporal abstraction and intrinsic motivation[C]// Proceedings of the 30th International Conference on Neural Information Processing Systems. Red Hook, NY : Curran Associates, Inc., 2016:1-9.
|
12 |
NAZARI M, OROOJLOOY A, SNYDER L V, et al. Reinforcement learning for solving the vehicle routing problem [C]// Proceedings of the 2017 International Conference on Advances in Neural Information Processing Systems. Red Hook, NY: Curran Associates, Inc.. 2018:9860-9870.
|
13 |
GAO L, CHEN M X, CHEN Q C, et al. Learn to design the heuristics for vehicle routing problem [EB/OL].[2020-02-20] . 10.48550/arXiv.2002.08539
|
14 |
张健,潘耀宗,杨海涛,等.基于蒙特卡洛Q值函数的多智能体决策方法[J].控制与决策,2020,35(3): 637-644. 10.13195/j.kzyjc.2018.0796
|
|
ZHANG J, PAN Y Z, YANG H T, et al. Multi-agent decision-making method based on Monte Carlo Q-value function [J]. Control and Decision, 2020, 35(3):637-644. 10.13195/j.kzyjc.2018.0796
|
15 |
SALAKHUTDINOV R, HINTON G. Replicated softmax: an undirected topic model [C]// Proceedings of the 2009 International Conference on Advances in Neural Information Processing Systems. Red Hook, NY: Curran Associates, Inc., 2009:1-8. 10.1016/j.ijar.2008.11.006
|
16 |
TRIVEDI R, DAI H J, WANG Y C, et al. Know-evolve: deep temporal reasoning for dynamic knowledge graphs [C]// Proceedings of the 34th International Conference on International Conference on Machine Learning. New York: JMLR.org. 2017:3462-3471.
|
17 |
ZAREMBA W, SUTSKEVER I, VINYALS O. Recurrent neural network regularization [EB/OL]. [2015-02-19]. . 10.3115/v1/p15-1002
|
18 |
HUANG G, LI Y X, PLEISS G, et al. Snapshot ensembles: train 1, get m for free [EB/OL]. [2017-04-01]. .
|
19 |
GLOROT X, BORDES A, BENGIO Y. Deep sparse rectifier neural networks [J]. Journal of Machine Learning Research, 2011, 15:315-323.
|
20 |
赵长鲜,方木云.基于贪心算法的物流配送系统的设计与实现[J].软件工程,2020,23(5):21-23.
|
|
ZHAO C X, FANG M Y. Design and implementation of logistics distribution system based on greedy algorithm[J]. Software Engineer, 2020, 23(5):21-23.
|
21 |
GLOROT X, BENGIO Y. Understanding the difficulty of training deep feedforward neural networks [C]// Proceedings of the 13th International Conference on Artificial Intelligence and Statistics. New York: PMLR.org, 2010:249-256.
|
22 |
KINGMA D P, BA J. Adam: a method for stochastic optimization [C]// Proceedings of the 2015 International Conference for Learning Representations. Irvine, CA: Universal Publishers, Inc., 2015:1-15.
|
23 |
NIKELSHPUR D, TAPPERT C C. Using particle swarm optimization to pre-train artificial neural networks: selecting initial training weights for feed-forward back-propagation neural networks [C]// Proceedings of the 2013 International Conference on Student-Faculty Research Day, Pace University. New York: Pace University, 2013:C5.1-C5.7.
|
24 |
KOOL W, HOOF V H, Attention WELLING M., learn to solve routing problems![C]// Proceedings of the 7th International Conference on International Conference on Learning Representations. London: Publications of HSE, 2019:1-25.
|
25 |
HELSGAUN K. An extension of the Lin-Kernighan-Helsgaun TSP solver for constrained traveling salesman and vehicle routing problems: technical report [R]. Roskilde: Roskilde University. 2017:1-60.
|