A self-learning approach for optimal feedback gains for finite-horizon nonlinear continuous time control systems is proposed and analysed. It relies on parameter dependent approximations to the optimal value function obtained from a family of universal approximators. The cost functional for the training of an approximate optimal feedback law incorporates two main features. First, it contains the average over the objective functional values of the parametrized feedback control for an ensemble of initial values. Second, it is adapted to exploit the relationship between the maximum principle and dynamic programming. Based on universal approximation properties, existence, convergence and first order optimality conditions for optimal neural network feedback controllers are proved.
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Mots clés : optimal feedback control, neural networks, Hamilton–Jacobi–Bellman equation, self-learning, reinforcement learning
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@article{CRMECA_2023__351_S1_535_0,
author = {Karl Kunisch and Daniel Walter},
title = {Optimal feedback control of dynamical systems via value-function approximation},
journal = {Comptes Rendus. M\'ecanique},
pages = {535--571},
publisher = {Acad\'emie des sciences, Paris},
volume = {351},
number = {S1},
year = {2023},
doi = {10.5802/crmeca.199},
language = {en},
}
TY - JOUR AU - Karl Kunisch AU - Daniel Walter TI - Optimal feedback control of dynamical systems via value-function approximation JO - Comptes Rendus. Mécanique PY - 2023 SP - 535 EP - 571 VL - 351 IS - S1 PB - Académie des sciences, Paris DO - 10.5802/crmeca.199 LA - en ID - CRMECA_2023__351_S1_535_0 ER -
Karl Kunisch; Daniel Walter. Optimal feedback control of dynamical systems via value-function approximation. Comptes Rendus. Mécanique, Volume 351 (2023) no. S1, pp. 535-571. doi : 10.5802/crmeca.199. https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.5802/crmeca.199/
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