PDP: parallel dynamic programming
Fei-Yue Wang; Jie Zhang; Qinglai Wei; Xinhu Zheng; Li Li
Source PublicationIEEE/CAA Journal of Automatica Sinica
2017
Volume4Issue:1Pages:1-5
AbstractDeep reinforcement learning is a focus research area
in artificial intelligence. The principle of optimality in dynamic
programming is a key to the success of reinforcement learning
methods. The principle of adaptive dynamic programming (ADP)
is first presented instead of direct dynamic programming (DP),
and the inherent relationship between ADP and deep reinforcement
learning is developed. Next, analytics intelligence, as the
necessary requirement, for the real reinforcement learning, is
discussed. Finally, the principle of the parallel dynamic programming,
which integrates dynamic programming and analytics
intelligence, is presented as the future computational intelligence.
KeywordParallel Dynamic Programming Dynamic Programming Adaptive Dynamic Programming Reinforcementlearning Deep Learning Neural Networks Artificial Intelligence.
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19739
Collection复杂系统管理与控制国家重点实验室_先进控制与自动化
Recommended Citation
GB/T 7714
Fei-Yue Wang,Jie Zhang,Qinglai Wei,et al. PDP: parallel dynamic programming[J]. IEEE/CAA Journal of Automatica Sinica,2017,4(1):1-5.
APA Fei-Yue Wang,Jie Zhang,Qinglai Wei,Xinhu Zheng,&Li Li.(2017).PDP: parallel dynamic programming.IEEE/CAA Journal of Automatica Sinica,4(1),1-5.
MLA Fei-Yue Wang,et al."PDP: parallel dynamic programming".IEEE/CAA Journal of Automatica Sinica 4.1(2017):1-5.
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