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pro vyhledávání: '"Arriojas, Argenis"'
In the field of reinforcement learning (RL), agents are often tasked with solving a variety of problems differing only in their reward functions. In order to quickly obtain solutions to unseen problems with new reward functions, a popular approach in
Externí odkaz:
http://arxiv.org/abs/2303.02557
In reinforcement learning (RL), the ability to utilize prior knowledge from previously solved tasks can allow agents to quickly solve new problems. In some cases, these new problems may be approximately solved by composing the solutions of previously
Externí odkaz:
http://arxiv.org/abs/2212.01174
Publikováno v:
Phys. Rev. Research 5, 023085 (2023)
Reinforcement learning (RL) is an important field of research in machine learning that is increasingly being applied to complex optimization problems in physics. In parallel, concepts from physics have contributed to important advances in RL with dev
Externí odkaz:
http://arxiv.org/abs/2106.03931
Akademický článek
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Akademický článek
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Autor:
Lou J; Department of Biology, Boston College, Chestnut Hill, MA, USA., Rezvani Y; Department of Mathematics, University of Massachusetts Boston, Boston, MA, USA.; Center for Personalized Cancer Therapy, University of Massachusetts Boston, Boston, MA, USA., Arriojas A; Center for Personalized Cancer Therapy, University of Massachusetts Boston, Boston, MA, USA., Wu Y; Department of Biology, Boston College, Chestnut Hill, MA, USA., Shankar N; Center for Personalized Cancer Therapy, University of Massachusetts Boston, Boston, MA, USA., Degras D; Department of Mathematics, University of Massachusetts Boston, Boston, MA, USA., Keroack CD; Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA, USA.; Department of Molecular Microbiology and Immunology, Brown University, Providence, RI, USA., Duraisingh MT; Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA, USA., Zarringhalam K; Department of Mathematics, University of Massachusetts Boston, Boston, MA, USA. kourosh.zarringhalam@umb.edu.; Center for Personalized Cancer Therapy, University of Massachusetts Boston, Boston, MA, USA. kourosh.zarringhalam@umb.edu., Gubbels MJ; Department of Biology, Boston College, Chestnut Hill, MA, USA. gubbelsj@bc.edu.
Publikováno v:
Nature communications [Nat Commun] 2024 Aug 28; Vol. 15 (1), pp. 7419. Date of Electronic Publication: 2024 Aug 28.
Autor:
Arriojas A; Department of Mathematics, University of Massachusetts Boston, Boston, MA 02125, USA.; Department of Physics, University of Massachusetts Boston, Boston, MA 02125, USA.; Center for Personalized Cancer Therapy, University of Massachusetts Boston, Boston, MA 02125, USA., Patalano S; Center for Personalized Cancer Therapy, University of Massachusetts Boston, Boston, MA 02125, USA., Macoska J; Center for Personalized Cancer Therapy, University of Massachusetts Boston, Boston, MA 02125, USA., Zarringhalam K; Department of Mathematics, University of Massachusetts Boston, Boston, MA 02125, USA.; Center for Personalized Cancer Therapy, University of Massachusetts Boston, Boston, MA 02125, USA.
Publikováno v:
NAR genomics and bioinformatics [NAR Genom Bioinform] 2023 Dec 13; Vol. 5 (4), pp. lqad106. Date of Electronic Publication: 2023 Dec 13 (Print Publication: 2023).