Titlebook: Neural Approximations for Optimal Control and Decision; Riccardo Zoppoli,Marcello Sanguineti,Thomas Parisi Book 2020 Springer Nature Switz

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Design of Mathematical Models by Learning From Data and FSP Functions,ionship by fixed-structure parametrized functions, the concepts of expected risk, empirical risk, and generalization error are described. The last error is then split into approximation and estimation errors. Four quantities of interest are emphasized: the accuracy, the number of arguments of the I/

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Numerical Methods for Integration and Search for Minima,r of random variables. Of course, integration includes the computation of the expected values of functions dependent on random variables. However, the latter shows peculiar nontrivial aspects that the former does not have. In case of a large number of random variables, the use of regular grids impli

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,Deterministic Optimal Control over a Finite Horizon,dom variables influence either the dynamic system or the cost function. Then, there is no necessity of estimating the state vector. Such optimization problems are stated for their intrinsic practical importance and to describe the basic concepts of dynamic programming. As the problems are formulated

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Team Optimal Control Problems,ormation and aim at minimizing a common cost functional. This organization can be described within the framework of “team theory.” Unlike the classical optimal control problems, linear-quadratic-Gaussian hypotheses are sufficient neither to obtain an optimal solution in closed-loop form nor to under