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

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機(jī)密

chondromalacia

Initiative

Design of Mathematical Models by Learning From Data and FSP Functions,es one to reduce the number of samples (under the same accuracy) and to obtain upper bounds on the errors in deterministic terms rather than in probabilistic ones. Deterministic learning relies on some basic quantities such as variation and discrepancy. Special families of deterministic sequences ca

aggressor

lattice

,Deterministic Optimal Control over?a?Finite Horizon,l growth of the number of samples, and thus to the curse of dimensionality. Therefore, the discretization by deterministic sequences of samples is addressed, which spread the samples in the most uniform way. Specifically, low-discrepancy sequences are considered, like quasi-Monte Carlo sequences. We

倫理學(xué)

集合

,Stochastic Optimal Control with?Imperfect State Information over a Finite Horizon,arameters. Of course, if the number of decision stages is large, the application of the ERIM is also impossible. Therefore, an approximate approach is followed by truncating the information vector and retaining in the memory only a suitable “l(fā)imited-memory information vector.”

Femish

Team Optimal Control Problems, takes particular forms. On the contrary, the “extended Ritz method” (ERIM) can be always applied. The ERIM consists in substituting the admissible functions with fixed-structure parametrized functions containing vectors of “free” parameters. The ERIM is tested in two case studies. The former is the

Directed

Optimal Control Problems over an Infinite Horizon, “extended Ritz method” and implemented through fixed-structure parametrized functions containing vectors of “free” parameters. Conditions are established on the maximum allowable approximation errors so as to ensure the boundedness of the state trajectories.