PDP: Parallel Dynamic Programming

被引:38
作者
FeiYue Wang [1 ,2 ,3 ,4 ]
Jie Zhang [1 ,5 ,6 ]
Qinglai Wei [1 ,7 ,3 ]
Xinhu Zheng [1 ,8 ]
Li Li [1 ,9 ]
机构
[1] IEEE
[2] State Key Laboratory of Management and Control for Complex Systems (SKL-MCCS), Institute of Automation, Chinese Academy of Sciences (CASIA)
[3] School of Computer and Control Engineering, University of Chinese Academy of Sciences
[4] Research Center for Military Computational Experiments and Parallel Systems Technology,National University of Defense Technology
[5] State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences (SKL-MCCS, CASIA)
[6] Qingdao Academy of Intelligent Industries
[7] State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences(SKL-MCCS, CASIA)
[8] Department of Computer Science and Engineering, University of Minnesota
[9] Department of Automation, Tsinghua University
关键词
Parallel dynamic programming; Dynamic programming; Adaptive dynamic programming; Reinforcement learning; Deep learning; Neural networks; Artificial intelligence;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
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.
引用
收藏
页码:1 / 5
页数:5
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