| PDP: parallel dynamic programming | |
Fei-Yue Wang ; Jie Zhang; Qinglai Wei; Xinhu Zheng; Li Li
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| Source Publication | IEEE/CAA Journal of Automatica Sinica
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| 2017 | |
| Volume | 4Issue:1Pages:1-5 |
| Abstract | 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. |
| Keyword | Parallel Dynamic Programming Dynamic Programming Adaptive Dynamic Programming Reinforcementlearning Deep Learning Neural Networks Artificial Intelligence. |
| Document Type | 期刊论文 |
| Identifier | http://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. |
| Files in This Item: | Download All | |||||
| File Name/Size | DocType | Version | Access | License | ||
| PDP Parallel Dynamic（3789KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | View Download | |
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