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Learning in real-world partially observable environments: Revisiting efficient deep Reinforcement Learning DVFS optimization for resource-constrained embedded devices.

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Bibliographic Details
Title: Learning in real-world partially observable environments: Revisiting efficient deep Reinforcement Learning DVFS optimization for resource-constrained embedded devices.
Authors: Hussain, Moayadeldin1, Lin, Man1, mlin@stfx.ca
Source: Journal of Systems Architecture; Jul2026, Vol. 176, pN.PAG-N.PAG, 1p
Database: Applied Science & Technology Source
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DbLabel: Applied Science & Technology Source
An: 194126139
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Learning in real-world partially observable environments: Revisiting efficient deep Reinforcement Learning DVFS optimization for resource-constrained embedded devices.
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=194126139
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.sysarc.2026.103797
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      – Code: eng
        Text: English
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        PageCount: 1
        StartPage: N.PAG
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      – TitleFull: Learning in real-world partially observable environments: Revisiting efficient deep Reinforcement Learning DVFS optimization for resource-constrained embedded devices.
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            NameFull: Hussain, Moayadeldin
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            NameFull: Lin, Man
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            – D: 01
              M: 07
              Text: Jul2026
              Type: published
              Y: 2026
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              Value: 176
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