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State following (StaF) kernel functions for function approximation Part I: Theory and motivation.

, , and . ACC, page 1217-1222. IEEE, (2015)

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Model-based reinforcement learning for on-line feedback-Nash equilibrium solution of N-player nonzero-sum differential games., , and . ACC, page 3000-3005. IEEE, (2014)State following (StaF) kernel functions for function approximation part II: Adaptive dynamic programming., , and . ACC, page 521-526. IEEE, (2015)Invariance-Like Results for Nonautonomous Switched Systems., , , , and . IEEE Trans. Automat. Contr., 64 (2): 614-627 (2019)Online Approximate Optimal Station Keeping of an Autonomous Underwater Vehicle., , and . CoRR, (2013)Inverse reinforcement learning in continuous time and space.. CoRR, (2018)The State Following Approximation Method., , and . IEEE Trans. Neural Netw. Learning Syst., 30 (6): 1716-1730 (2019)Safe Model-Based Reinforcement Learning for Systems With Parametric Uncertainties., , , and . Frontiers Robotics AI, (2021)Online approximate optimal path-following for a mobile robot., , , and . CDC, page 4536-4541. IEEE, (2014)Online inverse reinforcement learning for nonlinear systems., , and . CoRR, (2018)Model-based reinforcement learning for output-feedback optimal control of a class of nonlinear systems., , and . ACC, page 2378-2383. IEEE, (2019)