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Online scheduling of image satellites based on neural networks and deep reinforcement learning
WANG, Haijiao1,2; YANG, Zhen1,2; ZHOU, Wugen1,2; LI, Dalin2
作者部门空间技术部
发表期刊Chinese Journal of Aeronautics
2019
DOI10.1016/j.cja.2018.12.018
ISSN1000-9361
语种英语
关键词Deep reinforcement learning Dynamic scheduling Image satellites Neural network Online scheduling
摘要In the “Internet Plus” era, space-based information services require effective and fast image satellite scheduling. Most existing studies consider image satellite scheduling to be an optimization problem to solve with searching algorithms in a batch-wise manner. No real-time speed method for satellite scheduling exists. In this paper, with the idea of building a real-time speed method, satellite scheduling is remodeled based on a Dynamic and Stochastic Knapsack Problem (DSKP), and the objective is to maximize the total expected profit. No existing algorithm could be able to solve this novel scheduling problem properly. With inspiration from the recent achievements in Deep Reinforcement Learning (DRL) in video games, AlphaGo and dynamic controlling, a novel DRL-based method is applied to training a neural network to schedule tasks. The numerical results show that the method proposed in this paper can achieve relatively good performance with real-time speed and immediate respond style. © 2019
收录类别EI
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文献类型期刊论文
条目标识符http://ir.nssc.ac.cn/handle/122/6935
专题空间技术部
作者单位1.University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing; 100190, China;
2.National Space Science Center, Chinese Academy of Sciences, Beijing; 100190, China
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GB/T 7714
WANG, Haijiao,YANG, Zhen,ZHOU, Wugen,et al. Online scheduling of image satellites based on neural networks and deep reinforcement learning[J]. Chinese Journal of Aeronautics,2019.
APA WANG, Haijiao,YANG, Zhen,ZHOU, Wugen,&LI, Dalin.(2019).Online scheduling of image satellites based on neural networks and deep reinforcement learning.Chinese Journal of Aeronautics.
MLA WANG, Haijiao,et al."Online scheduling of image satellites based on neural networks and deep reinforcement learning".Chinese Journal of Aeronautics (2019).
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