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PDP: parallel dynamic programming
Author(s) -
Fei-Yue Wang,
Jie Zhang,
Qinglai Wei,
Xinhu Zheng,
Li Li
Publication year - 2017
Publication title -
ieee/caa journal of automatica sinica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.277
H-Index - 41
eISSN - 2329-9274
pISSN - 2329-9266
DOI - 10.1109/jas.2017.7510310
Subject(s) - computing and processing , communication, networking and broadcast technologies , general topics for engineers , robotics and control systems
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 U+0028 ADP U+0029 is first presented instead of direct dynamic programming U+0028 DP U+0029, 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.

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