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Reinforcement Procedure for Randomized Machine Learning

Author

Listed:
  • Yuri S. Popkov

    (Federal Research Center “Computer Science and Control” of Russian Academy of Sciences, 44/2 Vavilova, 119333 Moscow, Russia
    Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, 65 Profsoyuznaya, 117997 Moscow, Russia)

  • Yuri A. Dubnov

    (Federal Research Center “Computer Science and Control” of Russian Academy of Sciences, 44/2 Vavilova, 119333 Moscow, Russia
    Faculty of Computer Science, National Research University “Higher Schools of Economics”, 20 Myasnitskaya, 109028 Moscow, Russia)

  • Alexey Yu. Popkov

    (Federal Research Center “Computer Science and Control” of Russian Academy of Sciences, 44/2 Vavilova, 119333 Moscow, Russia)

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.

Suggested Citation

  • Yuri S. Popkov & Yuri A. Dubnov & Alexey Yu. Popkov, 2023. "Reinforcement Procedure for Randomized Machine Learning," Mathematics, MDPI, vol. 11(17), pages 1-14, August.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:17:p:3651-:d:1223667
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    References listed on IDEAS

    as
    1. Marco Avellaneda, 1998. "Minimum-Relative-Entropy Calibration of Asset-Pricing Models," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 1(04), pages 447-472.
    2. Yuri S. Popkov & Alexey Yu. Popkov & Yuri A. Dubnov & Dimitri Solomatine, 2020. "Entropy-Randomized Forecasting of Stochastic Dynamic Regression Models," Mathematics, MDPI, vol. 8(7), pages 1-20, July.
    3. Yuri S. Popkov & Yuri A. Dubnov & Alexey Yu. Popkov, 2016. "New Method of Randomized Forecasting Using Entropy-Robust Estimation: Application to the World Population Prediction," Mathematics, MDPI, vol. 4(1), pages 1-16, March.
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