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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)  
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.