Computer Science ›› 2019, Vol. 46 ›› Issue (10): 265-272.doi: 10.11896/jsjkx.180901655
• Artificial Intelligence • Previous Articles Next Articles
CHEN Jian-ping1,2,3, ZOU Feng1,2,3, LIU Quan4, WU Hong-jie1,2,3, HU Fu-yuan1,2,3, FU Qi-ming1,2,3
CLC Number:
[1]SUTTON R S,BARTO A G.Reinforcement learning:An introduction[M].Cambridge:MIT Press,1998. [2]PUTERMAN M.Markov decision process [J].Statistica Neerlandica,1985,39(2):219-233. [3]WU Y,SHEN T.Policy Iteration algorithm for optimal control of stochastic logical dynamical systems [J].IEEE Transactions on Neural Networks & Learning Systems,2017,28(99):1-6. [4]WEI Q,LIU D,LIN H.Value iteration adaptive dynamic programming for optimal control of discrete-time nonlinear systems [J].IEEE Transactions on Cybernetics,2016,46(3):840-853. [5]BRADTKE S J,BARTO A G.Linear least-squares algorithms for temporal difference learning [J].Machine Learning,1996,22(1/2/3):33-57. [6]HACHIYA H,AKIYAMA T,SUGIAYMA M,et al.Adaptive importance sampling for value function approximation in off-po-licy reinforcement learning [J].Neural Networks,2009,22(10):1399-1410. [7]MAHMOOD A R,SUTTON R S.Off-policy learning based on weighted importance sampling with linear computational complexity[C]//Proceedings of the 31st International Conference on Uncertainty in Artificial Intelligence.Amsterdam:AUAI,2015:552-561. [8]CHEN X L,CAO L,LI C X,et al.Deep reinforcement learning via good choice resampling experience replay memory [J].Control and Decision,2018,33(4):129-134. [9]LEDIG C,THEIS L,HUSZÁR F,et al.Photo-realistic single image super-resolution using a generative adversarial network[C]//Proceedings of the 30th IEEE Conference on ComputerVision and Pattern Recognition.Hawaii:IEEE,2017:105-114. [10]CAO Z Y,NIU S Z,ZHANG J W.Masked image inpainting algorithm based on generative adversarial networks [J].Journal of Beijing University of Posts and Telecom,2018,41(3):81-86.(in Chinese) 曹志义,牛少彰,张继威.基于生成对抗网络的遮挡图像修复算法[J].北京邮电大学学报,2018,41(3):81-86. [11]ZHENG W B,WANG K F,WANG F Y.Background subtraction algorithm with bayesian generative adversarial networks [J].Acta Automatica Sinica,2018,44(5):878-890.(in Chinese) 郑文博,王坤峰,王飞跃.基于贝叶斯生成对抗网络的背景消减算法[J].自动化学报,2018,44(5):878-890. [12]ZHANG Y Z,GAN Z,CARIN L.Generating text via adversarial training[C]//Proceedings of the 30th Conference on Neural Information Processing Systems.Barcelona:MIT Press,2016:1543-1551. [13]REED S,AKATA Z,YAN X C,et al.Generative adver-sarial text to image synthesis[C]//Proceedings of the 33rd International Conference on Machine Learning.New York:ACM,2016:1060-1069. [14]WANG K F,GOU C,DUAN Y J,et al.Generative adversarial networks:the state of the art and beyond[J].Acta Automatica Sinica,2017,43(3):321-332.(in Chinese) 王坤峰,苟超,段艳杰,等.生成式对抗网络GAN的研究进展与展望[J].自动化学报,2017,43(3):321-332. [15]ARJVSKY M,CHINTALA S,BOTTOU L.Wasserstein gene-rative adversarial networks[C]//Proceedings of the 34th International Conference on Machine Learning.Sydney:ACM,2017:214-223. [16]MIRZA M,OSINDERO S.Conditional generative adversarial nets [J].Computer Science,2014,8(13):2672-2680. [17]LECUN Y,BENGIO Y,HINTON G.Deep learning[J].Nature,2015,521(7553):436-444. [18]MNIH V,KAVUKCUOGLU K,SILVER D,et al.Human-level control through deep reinforcement learning [J].Nature,2015,518(7540):529-533. |
[1] | RAO Zhi-shuang, JIA Zhen, ZHANG Fan, LI Tian-rui. Key-Value Relational Memory Networks for Question Answering over Knowledge Graph [J]. Computer Science, 2022, 49(9): 202-207. |
[2] | LIU Xing-guang, ZHOU Li, LIU Yan, ZHANG Xiao-ying, TAN Xiang, WEI Ji-bo. Construction and Distribution Method of REM Based on Edge Intelligence [J]. Computer Science, 2022, 49(9): 236-241. |
[3] | TANG Ling-tao, WANG Di, ZHANG Lu-fei, LIU Sheng-yun. Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy [J]. Computer Science, 2022, 49(9): 297-305. |
[4] | XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai. Temporal Knowledge Graph Representation Learning [J]. Computer Science, 2022, 49(9): 162-171. |
[5] | SHI Dian-xi, ZHAO Chen-ran, ZHANG Yao-wen, YANG Shao-wu, ZHANG Yong-jun. Adaptive Reward Method for End-to-End Cooperation Based on Multi-agent Reinforcement Learning [J]. Computer Science, 2022, 49(8): 247-256. |
[6] | WANG Jian, PENG Yu-qi, ZHAO Yu-fei, YANG Jian. Survey of Social Network Public Opinion Information Extraction Based on Deep Learning [J]. Computer Science, 2022, 49(8): 279-293. |
[7] | HAO Zhi-rong, CHEN Long, HUANG Jia-cheng. Class Discriminative Universal Adversarial Attack for Text Classification [J]. Computer Science, 2022, 49(8): 323-329. |
[8] | JIANG Meng-han, LI Shao-mei, ZHENG Hong-hao, ZHANG Jian-peng. Rumor Detection Model Based on Improved Position Embedding [J]. Computer Science, 2022, 49(8): 330-335. |
[9] | SUN Qi, JI Gen-lin, ZHANG Jie. Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection [J]. Computer Science, 2022, 49(8): 172-177. |
[10] | YUAN Wei-lin, LUO Jun-ren, LU Li-na, CHEN Jia-xing, ZHANG Wan-peng, CHEN Jing. Methods in Adversarial Intelligent Game:A Holistic Comparative Analysis from Perspective of Game Theory and Reinforcement Learning [J]. Computer Science, 2022, 49(8): 191-204. |
[11] | HU Yan-yu, ZHAO Long, DONG Xiang-jun. Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification [J]. Computer Science, 2022, 49(7): 73-78. |
[12] | CHENG Cheng, JIANG Ai-lian. Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction [J]. Computer Science, 2022, 49(7): 120-126. |
[13] | HOU Yu-tao, ABULIZI Abudukelimu, ABUDUKELIMU Halidanmu. Advances in Chinese Pre-training Models [J]. Computer Science, 2022, 49(7): 148-163. |
[14] | ZHOU Hui, SHI Hao-chen, TU Yao-feng, HUANG Sheng-jun. Robust Deep Neural Network Learning Based on Active Sampling [J]. Computer Science, 2022, 49(7): 164-169. |
[15] | SU Dan-ning, CAO Gui-tao, WANG Yan-nan, WANG Hong, REN He. Survey of Deep Learning for Radar Emitter Identification Based on Small Sample [J]. Computer Science, 2022, 49(7): 226-235. |
|