PDP: parallel dynamic programming
Fei-Yue Wang; Jie Zhang; Qinglai Wei; Xinhu Zheng; Li Li
2017
发表期刊IEEE/CAA Journal of Automatica Sinica
卷号4期号:1页码:1-5
摘要Deep 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.
关键词Parallel Dynamic Programming Dynamic Programming Adaptive Dynamic Programming Reinforcementlearning Deep Learning Neural Networks Artificial Intelligence.
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/19739
专题复杂系统管理与控制国家重点实验室_先进控制与自动化
推荐引用方式
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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