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Online reinforcement learning for continuous-state systems 专著章节/文集论文
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Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, 2014
作者:  Yuanheng Zhu;  Zhao DB(赵冬斌)
Adobe PDF(24150Kb)  |  收藏  |  浏览/下载：280/36  |  提交时间：2017/09/13
ITS With Complete Traffic Control 期刊论文
IEEE Transactions on Intelligent Transportation Systems, 2014, 卷号: 15, 期号: 2, 页码: 457-462
作者:  Fei-Yue Wang
浏览  |  Adobe PDF(76Kb)  |  收藏  |  浏览/下载：194/100  |  提交时间：2017/03/07
Traffic Control  Its  
Online Synchronous Approximate Optimal Learning Algorithm for Multiplayer Nonzero-Sum Games With Unknown Dynamics 期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2014, 卷号: 44, 期号: 8, 页码: 1015-1027
作者:  Liu, Derong;  Li, Hongliang;  Wang, Ding
Adobe PDF(20912Kb)  |  收藏  |  浏览/下载：251/85  |  提交时间：2015/08/12
Adaptive Dynamic Programming (Adp)  Approximate Dynamic Programming  Multiplayer Nonzero-sum Games  Neural Networks  Neuro-dynamic Programming  Policy Iteration  
Data-Driven Neuro-Optimal Temperature Control of Water-Gas Shift Reaction Using Stable Iterative Adaptive Dynamic Programming 期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2014, 卷号: 61, 期号: 11, 页码: 6399-6408
作者:  Wei, Qinglai;  Liu, Derong;  Derong Liu
浏览  |  Adobe PDF(664Kb)  |  收藏  |  浏览/下载：297/109  |  提交时间：2015/08/12
Adaptive Critic Designs  Adaptive Dynamic Programming (Adp)  Approximate Dynamic Programming  Approximation Errors  Data-driven Control  Neural Networks (Nns)  Optimal Control  Reinforcement Learning  Water-gas Shift (Wgs)  
Integral Reinforcement Learning for Linear Continuous-Time Zero-Sum Games With Completely Unknown Dynamics 期刊论文
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2014, 卷号: 11, 期号: 3, 页码: 706-714
作者:  Li, Hongliang;  Liu, Derong;  Wang, Ding
Adobe PDF(1753Kb)  |  收藏  |  浏览/下载：313/106  |  提交时间：2015/08/12
Adaptive Critic Designs  Adaptive Dynamic Programming  Approximate Dynamic Programming  Reinforcement Learning  Policy Iteration  Zero-sum Games  
Scanning the Issue and Beyond: ITS With Complete Traffic Control 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2014, 卷号: 15, 期号: 2, 页码: 457-462
作者:  Wang, Fei-Yue
浏览  |  Adobe PDF(76Kb)  |  收藏  |  浏览/下载：112/20  |  提交时间：2015/08/12
Traffic Control