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Comprehensive comparison of online ADP algorithms for continuous-time optimal control 期刊论文
ARTIFICIAL INTELLIGENCE REVIEW, 2018, 卷号: 49, 期号: 4, 页码: 531-547
作者:  Zhu, Yuanheng;  Zhao, Dongbin
Adobe PDF(766Kb)  |  收藏  |  浏览/下载：413/183  |  提交时间：2017/09/13
Adaptive Dynamic Programming  Policy Iteration  Integral Reinforcement Learning  Experience Replay  Off-policy  
Online reinforcement learning for continuous-state systems 专著章节/文集论文
出自: Frontiers of Intelligent Control and Information Processing, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore: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：251/27  |  提交时间：2017/09/13
Iterative Adaptive Dynamic Programming for Solving Unknown Nonlinear Zero-Sum Game Based on Online Data 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2017, 卷号: 28, 期号: 3, 页码: 714-725
作者:  Zhu, Yuanheng;  Zhao, Dongbin;  Li, Xiangjun
浏览  |  Adobe PDF(547Kb)  |  收藏  |  浏览/下载：448/186  |  提交时间：2017/05/05
Adaptive Dynamic Programming (Adp)  H-infinity Control  Policy Iteration (Pi)  Zero-sum Game (Zsg)  
Data-Based Adaptive Critic Designs for Nonlinear Robust Optimal Control With Uncertain Dynamics 期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2016, 卷号: 46, 期号: 11, 页码: 1544-1555
作者:  Wang, Ding;  Liu, Derong;  Zhang, Qichao;  Zhao, Dongbin
浏览  |  Adobe PDF(1082Kb)  |  收藏  |  浏览/下载：461/201  |  提交时间：2017/02/14
Adaptive Critic Designs  Adaptive Dynamic Programming  Intelligent Control  Neural Networks  Policy Iteration  Robust Optimal Control  System Identification  Uncertain Nonlinear Systems  
Online Synchronous Policy Iteration Based on Concurrent Learning to Solve Continuous-time Optimal Control Problem 会议论文
Proceedings of International Conference on Information Science and Technology, Changsha, 2015.4.25~4.27
作者:  Haitao Wang;  Dongbin Zhao;  Chengdong Li
浏览  |  Adobe PDF(1339Kb)  |  收藏  |  浏览/下载：271/100  |  提交时间：2016/06/15
Experience Replay for Optimal Control of Nonzero-Sum Game Systems With Unknown Dynamics 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2016, 卷号: 46, 期号: 3, 页码: 854-865
作者:  Zhao, Dongbin;  Zhang, Qichao;  Wang, Ding;  Zhu, Yuanheng
浏览  |  Adobe PDF(1769Kb)  |  收藏  |  浏览/下载：512/197  |  提交时间：2016/06/14
Adaptive Dynamic Programming (Adp)  Experience Replay  Nonzero-sum (Nzs) Games  Optimal Control  Unknown Dynamics  
Model-Free Optimal Control for Affine Nonlinear Systems With Convergence Analysis 期刊论文
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2015, 卷号: 12, 期号: 4, 页码: 1461-1468
作者:  Zhao, Dongbin;  Xia, Zhongpu;  Wang, Ding
浏览  |  Adobe PDF(1985Kb)  |  收藏  |  浏览/下载：349/89  |  提交时间：2015/11/12
Action Dependent Heuristic Dynamic Programming  Adaptive Dynamic Programming  Model-free Optimal Control  Neural Networks  Policy Iteration  
MEC-A Near-Optimal Online Reinforcement Learning Algorithm for Continuous Deterministic Systems 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2015, 卷号: 26, 期号: 2, 页码: 346-356
作者:  Zhao, Dongbin;  Zhu, Yuanheng
浏览  |  Adobe PDF(2156Kb)  |  收藏  |  浏览/下载：272/111  |  提交时间：2015/09/18
Efficient Exploration  Probably Approximately Correct (Pac)  Reinforcement Learning (Rl)  State Aggregation  
Adaptive dynamic programming for optimal control of unknown nonlinear discrete-time systems 会议论文
Symposium Series on Computational Intelligence (SSC) - IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), 2011
作者:  Liu, Derong;  Wang, Ding;  Zhao, Dongbin
Adobe PDF(1951Kb)  |  收藏  |  浏览/下载：191/47  |  提交时间：2015/08/19
Optimal control of unknown nonaffine nonlinear discrete-time systems based on adaptive dynamic programming 期刊论文
AUTOMATICA, 2012, 卷号: 48, 期号: 8, 页码: 1825-1832
作者:  Wang, Ding;  Liu, Derong;  Wei, Qinglai;  Zhao, Dongbin;  Jin, Ning
Adobe PDF(598Kb)  |  收藏  |  浏览/下载：374/145  |  提交时间：2015/08/12
Adaptive Critic Designs  Adaptive Dynamic Programming  Approximate Dynamic Programming  Globalized Dual Heuristic Programming  Intelligent Control  Neural Network  Optimal Control