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• W2790688051 abstract "An infinite-horizon optimal regulation problem for a control-affine deterministic system is solved online using a local state following (StaF) kernel and a regional model-based reinforcement learning (R-MBRL) method to approximate the value function. Unlike traditional methods such as R-MBRL that aim to approximate the value function over a large compact set, the StaF kernel approach aims to approximate the value function in a local neighborhood of the state that travels within a compact set. In this paper, the value function is approximated using a state-dependent convex combination of the StaF-based and the R-MBRL-based approximations. As the state enters a neighborhood containing the origin, the value function transitions from being approximated by the StaF approach to the R-MBRL approach. Semiglobal uniformly ultimately bounded (SGUUB) convergence of the system states to the origin is established using a Lyapunov-based analysis. Simulation results are provided for two, three, six, and ten-state dynamical systems to demonstrate the scalability and performance of the developed method." @default.
• W2790688051 created "2018-03-29" @default.
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• W2790688051 date "2018-06-01" @default.
• W2790688051 modified "2023-10-18" @default.
• W2790688051 title "Approximate Dynamic Programming: Combining Regional and Local State Following Approximations" @default.
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• W2790688051 doi "https://doi.org/10.1109/tnnls.2018.2808102" @default.
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• W2790688051 hasPublicationYear "2018" @default.
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