The New Geometric "State-Action" Space Representation for Q-Learning Algorithm for Protein Structure Folding Problem

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Бібліографічні деталі
Дата:2020
Автор: S. A. Chornozhuk
Формат: Стаття
Мова:Англійська
Опубліковано: 2020
Назва видання:Cybernetics and Computer Technologies
Онлайн доступ:http://jnas.nbuv.gov.ua/article/UJRN-0001180907
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Назва журналу:Library portal of National Academy of Sciences of Ukraine | LibNAS

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Library portal of National Academy of Sciences of Ukraine | LibNAS
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author S. A. Chornozhuk
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spelling open-sciencenbuvgovua-139442024-02-25T18:01:08Z The New Geometric "State-Action" Space Representation for Q-Learning Algorithm for Protein Structure Folding Problem S. A. Chornozhuk 2707-4501 2020 en Cybernetics and Computer Technologies http://jnas.nbuv.gov.ua/article/UJRN-0001180907 Article
spellingShingle Cybernetics and Computer Technologies
S. A. Chornozhuk
The New Geometric "State-Action" Space Representation for Q-Learning Algorithm for Protein Structure Folding Problem
title The New Geometric "State-Action" Space Representation for Q-Learning Algorithm for Protein Structure Folding Problem
title_full The New Geometric "State-Action" Space Representation for Q-Learning Algorithm for Protein Structure Folding Problem
title_fullStr The New Geometric "State-Action" Space Representation for Q-Learning Algorithm for Protein Structure Folding Problem
title_full_unstemmed The New Geometric "State-Action" Space Representation for Q-Learning Algorithm for Protein Structure Folding Problem
title_short The New Geometric "State-Action" Space Representation for Q-Learning Algorithm for Protein Structure Folding Problem
title_sort new geometric "state-action" space representation for q-learning algorithm for protein structure folding problem
url http://jnas.nbuv.gov.ua/article/UJRN-0001180907
work_keys_str_mv AT sachornozhuk thenewgeometricampquotstateactionampquotspacerepresentationforqlearningalgorithmforproteinstructurefoldingproblem
AT sachornozhuk newgeometricampquotstateactionampquotspacerepresentationforqlearningalgorithmforproteinstructurefoldingproblem