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Potential-based multiobjective reinforcement learning approaches to low-impact agents for AI safety.

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Title: Potential-based multiobjective reinforcement learning approaches to low-impact agents for AI safety.
Authors: Vamplew, Peter1, p.vamplew@federation.edu.au, Foale, Cameron1, Dazeley, Richard2, Bignold, Adam1
Source: Engineering Applications of Artificial Intelligence; Apr2021, Vol. 100, pN.PAG-N.PAG, 1p
Database: Applied Science & Technology Source
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  Availability: 0
Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 149177038
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Potential-based multiobjective reinforcement learning approaches to low-impact agents for AI safety.
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=149177038
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.engappai.2021.104186
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      – Code: eng
        Text: English
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        PageCount: 1
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      – TitleFull: Potential-based multiobjective reinforcement learning approaches to low-impact agents for AI safety.
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            NameFull: Dazeley, Richard
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              Text: Apr2021
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              Y: 2021
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              Value: 100
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