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Doklady Rossijskoj Akademii Nauk. Mathematika, Informatika, Processy Upravlenia, 2024, Volume 520, Number 2, Pages 251–259
DOI: https://doi.org/10.31857/S2686954324700619
(Mi danma604)
 

SPECIAL ISSUE: ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING TECHNOLOGIES

Environments for automatic curriculum learning: a short survey

M. I. Nesterovaabc, A. A. Skrynnikac, A. I. Panovabc

a Federal Research Center "Computer Science and Control" of Russian Academy of Sciences, Moscow, Russia
b Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Moscow Region
c Artificial Intelligence Research Institute, Moscow, Russia
DOI: https://doi.org/10.31857/S2686954324700619
Abstract: Reinforcement learning encompasses various approaches that involve training an agent on multiple tasks. These approaches include training a general agent capable of executing a wide range of tasks and training a specialized agent focused on mastering a specific skill. Curriculum learning strategically orders tasks to optimize the learning process, enhancing training efficiency and improving overall performance. Researchers developing novel methods must select appropriate environments for evaluation and comparison with other methods. We introduce an overview of environments suitable for assessing curriculum learning methods, highlighting their key differences. This work details task components, modifications, and a classification of existing curriculum learning methods. We aim to provide researchers with valuable insights into the selection and utilization of environments for evaluating curriculum learning approaches.
Keywords: reinforcement learning, curriculum learning, multi-task learning, deep learning.
Funding agency Grant number
Ministry of Science and Higher Education of the Russian Federation 075-15-2024-544
This work was supported by the Ministry of Science and Higher Education of the Russian Federation under Project 075-15-2024-544.
Received: 20.09.2024
Accepted: 02.10.2024
English version:
Doklady Mathematics, 2024, Volume 110, Issue suppl. 1, Pages S223–S229
DOI: https://doi.org/10.1134/S1064562424602099
Bibliographic databases:
Document Type: Article
UDC: 517.977
Language: Russian
Citation: M. I. Nesterova, A. A. Skrynnik, A. I. Panov, “Environments for automatic curriculum learning: a short survey”, Dokl. RAN. Math. Inf. Proc. Upr., 520:2 (2024), 251–259; Dokl. Math., 110:suppl. 1 (2024), S223–S229
Citation in format AMSBIB
\Bibitem{NesSkrPan24}
\by M.~I.~Nesterova, A.~A.~Skrynnik, A.~I.~Panov
\paper Environments for automatic curriculum learning: a short survey
\jour Dokl. RAN. Math. Inf. Proc. Upr.
\yr 2024
\vol 520
\issue 2
\pages 251--259
\mathnet{http://mi.mathnet.ru/danma604}
\elib{https://elibrary.ru/item.asp?id=80287452}
\transl
\jour Dokl. Math.
\yr 2024
\vol 110
\issue suppl. 1
\pages S223--S229
\crossref{https://doi.org/10.1134/S1064562424602099}
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