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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
Authors:  Zhu, Yuanheng;  Zhao, Dongbin
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Adaptive Dynamic Programming  Policy Iteration  Integral Reinforcement Learning  Experience Replay  Off-policy  
Deep Reinforcement Learning With Visual Attention for Vehicle Classification 期刊论文
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, 2017, 卷号: 9, 期号: 4, 页码: 356-367
Authors:  Zhao, Dongbin;  Chen, Yaran;  Lv, Le
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Convolutional Neural Network (Cnn)  Reinforcement Learning  Vehicle Classification  Visual Attention  
Event-Triggered H-infinity Control for Continuous-Time Nonlinear System via Concurrent Learning 期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2017, 卷号: 47, 期号: 7, 页码: 1071-1081
Authors:  Zhang, Qichao;  Zhao, Dongbin;  Zhu, Yuanheng
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Concurrent Learning  Event-triggered Control  H-infinity Optimal Control  Neural Networks (Nns)  Zero-sum (Zs) Game  
Data-driven adaptive dynamic programming for continuous-time fully cooperative games with partially constrained inputs 期刊论文
NEUROCOMPUTING, 2017, 卷号: 238, 期号: *, 页码: 377-386
Authors:  Zhang, Qichao;  Zhao, Dongbin;  Zhu, Yuanheng
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Adaptive Dynamic Programming  Optimal Control  Neural Network  Fully Cooperative Games  Data-driven  Constrained Input  
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
Authors:  Zhu, Yuanheng;  Zhao, Dongbin;  Li, Xiangjun
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Adaptive Dynamic Programming (Adp)  H-infinity Control  Policy Iteration (Pi)  Zero-sum Game (Zsg)  
Online reinforcement learning control by Bayesian inference 期刊论文
IET CONTROL THEORY AND APPLICATIONS, 2016, 卷号: 10, 期号: 12, 页码: 1331-1338
Authors:  Xia, Zhongpu;  Zhao, Dongbin;  Dongbin Zhao
View  |  Adobe PDF(1559Kb)  |  Favorite  |  View/Download:119/43  |  Submit date:2016/06/15
Learning Systems  Bayes Methods  Gaussian Processes  Optimal Control  Online Reinforcement Learning Control  Bayesian Inference  Self-learning Control  Probability  Action Value Function  Gaussian Process  Bayesian-state-action-reward-state-action Algorithm  
Using reinforcement learning techniques to solve continuous-time non-linear optimal tracking problem without system dynamics 期刊论文
IET CONTROL THEORY AND APPLICATIONS, 2016, 卷号: 10, 期号: 12, 页码: 1339-1347
Authors:  Zhu, Yuanheng;  Zhao, Dongbin;  Li, Xiangjun
View  |  Adobe PDF(976Kb)  |  Favorite  |  View/Download:171/73  |  Submit date:2016/12/26
Nonlinear Control Systems  Continuous Time Systems  Learning (Artificial Intelligence)  Optimal Control  Dynamic Programming  Lyapunov Methods  Linear Systems  Reinforcement Learning  Continuous-time Problem  Nonlinear Optimal Tracking Problem  Adaptive Dynamic Programming  Model-free Adaptive Optimal Tracking Algorithm  Lyapunov Analysis  Linear System