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Adaptive Reinforcement Learning Control Based on Neural Approximation for Nonlinear Discrete-Time Systems With Unknown Nonaffine Dead-Zone Input Journal article
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019,Volume: 30,Issue: 1,Page: 295-305
Authors:  Liu, Yan-Jun;  Li, Shu;  Tong, Shaocheng;  Chen, C. L. Philip
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Discrete-time systems  neural networks (NNs)  nonlinear systems  optimal control  reinforcement learning  
Adaptive Reinforcement Learning Control Based on Neural Approximation for Nonlinear Discrete-Time Systems with Unknown Nonaffine Dead-Zone Input Journal article
IEEE Transactions on Neural Networks and Learning Systems, 2019,Volume: 30,Issue: 1,Page: 295-305
Authors:  Liu Y.-J.;  Li S.;  Tong S.;  Chen C.L.P.
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Discrete-time systems  neural networks (NNs)  nonlinear systems  optimal control  reinforcement learning  
Adaptive Critic Design for Pure-Feedback Discrete-Time MIMO Systems Preceded by Unknown Backlashlike Hysteresis Journal article
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018,Volume: 29,Issue: 11,Page: 5681-5690
Authors:  Tang, Li;  Liu, Yan-Jun;  Chen, C. L. Philip
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Adaptive critic design (ACD)  backlashlike hysteresis  neural networks (NNs)  nonlinear pure-feedback systems  optimal control  
Optimized Multi-Agent Formation Control Based on an Identifier-Actor--Critic Reinforcement Learning Algorithm Journal article
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2018,Volume: 26,Issue: 5,Page: 2719-2731
Authors:  Wen, Guoxing;  Chen, C. L. Philip;  Feng, Jun;  Zhou, Ning
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Fuzzy logic systems (FLSs)  identifier-actor-critic architecture  multi-agent formation  optimized formation control  reinforcement learning (RL)  
Optimized Multi-Agent Formation Control Based on an Identifier-Actor-Critic Reinforcement Learning Algorithm Journal article
IEEE Transactions on Fuzzy Systems, 2018,Volume: 26,Issue: 5,Page: 2719-2731
Authors:  Wen G.;  Chen C.L.P.;  Feng J.;  Zhou N.
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Fuzzy logic systems (FLSs)  identifier-actor-critic architecture  multi-agent formation  optimized formation control  reinforcement learning (RL)  
Simulation framework of ubiquitous network environments for designing diverse network robots Journal article
Future Generation Computer Systems, 2017,Volume: 76,Page: 468-473
Authors:  Cho, Seoungjae;  Fong, Simon;  Park, Yong Woon;  Cho, Kyungeun
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Human-robot Interaction  Network Robot  Planning  Reinforcement Learning  Simulation  Smart Home  
Reinforcement learning design-based adaptive tracking control with less learning parameters for nonlinear discrete-time MIMO systems Journal article
IEEE Transactions on Neural Networks and Learning Systems, 2015,Volume: 26,Issue: 1,Page: 165
Authors:  Liu Y.-J.;  Tang L.;  Tong S.;  Chen C.L.P.;  Li D.-J.
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Adaptive control  discrete-time systems  online approximators  reinforcement learning (RL)  uncertain nonlinear systems.  
Learning-based web service composition in uncertain environments Journal article
Journal of Web Engineering, 2014,Volume: 13,Issue: 5-6,Page: 450-468
Authors:  Yu L.;  Wang Z.;  Meng L.;  Qiu X.;  Zhou J.
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Optimal policy  Partially observable markov decision process  Reinforcement learning algorithm  Success rate of service composition  Web service composition  
Adaptive consensus control for a class of nonlinear multiagent time-delay systems using neural networks Journal article
IEEE Transactions on Neural Networks and Learning Systems, 2014,Volume: 25,Issue: 6,Page: 1217
Authors:  Chen C.L.P.;  Wen G.-X.;  Liu Y.-J.;  Wang F.-Y.
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Consensus control  Lyapunov-Krasovskii functional  Neural networks (NNS)  Nonlinear multiagent systems  Time delay  
Computer-controlled graphical avatars and reinforcement learning Book
2012
Authors:  He Y.;  Tang Y.Y.
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