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An empirical investigation of value-based multi-objective reinforcement learning for stochastic environments.

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Title: An empirical investigation of value-based multi-objective reinforcement learning for stochastic environments.
Authors: Ding, Kewen1, Vamplew, Peter1,2, p.vamplew@federation.edu.au, Foale, Cameron1, Dazeley, Richard2
Source: Knowledge Engineering Review; 2025, Vol. 40, p1-29, 29p
Database: Applied Science & Technology Source
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  Availability: 0
Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 192815877
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: An empirical investigation of value-based multi-objective reinforcement learning for stochastic environments.
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  Data: <searchLink fieldCode="JN" term="%22Knowledge+Engineering+Review%22">Knowledge Engineering Review</searchLink>; 2025, Vol. 40, p1-29, 29p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=192815877
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1017/S0269888925100052
    Languages:
      – Code: eng
        Text: English
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        PageCount: 29
        StartPage: 1
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      – TitleFull: An empirical investigation of value-based multi-objective reinforcement learning for stochastic environments.
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            NameFull: Ding, Kewen
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            NameFull: Vamplew, Peter
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            NameFull: Foale, Cameron
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            NameFull: Dazeley, Richard
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              Text: 2025
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              Y: 2025
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              Value: 40
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