Reinforcement Procedure for Randomized Machine Learning

Yuri S. Popkov (), Yuri A. Dubnov and Alexey Yu. Popkov
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Yuri S. Popkov: Federal Research Center “Computer Science and Control” of Russian Academy of Sciences, 44/2 Vavilova, 119333 Moscow, Russia
Yuri A. Dubnov: Federal Research Center “Computer Science and Control” of Russian Academy of Sciences, 44/2 Vavilova, 119333 Moscow, Russia
Alexey Yu. Popkov: Federal Research Center “Computer Science and Control” of Russian Academy of Sciences, 44/2 Vavilova, 119333 Moscow, Russia

Mathematics, 2023, vol. 11, issue 17, 1-14

Abstract: This paper is devoted to problem-oriented reinforcement methods for the numerical implementation of Randomized Machine Learning. We have developed a scheme of the reinforcement procedure based on the agent approach and Bellman’s optimality principle. This procedure ensures strictly monotonic properties of a sequence of local records in the iterative computational procedure of the learning process. The dependences of the dimensions of the neighborhood of the global minimum and the probability of its achievement on the parameters of the algorithm are determined. The convergence of the algorithm with the indicated probability to the neighborhood of the global minimum is proved.

Keywords: randomized machine learning; reinforcement learning; utility function; payoff function; Bellman’s optimality principle (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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