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Enhancing multi-agent reinforcement learning via world model assisted single-agent population policies in multi-UAV cooperative-competitive scenario.

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Title: Enhancing multi-agent reinforcement learning via world model assisted single-agent population policies in multi-UAV cooperative-competitive scenario.
Authors: Cheng, Jiaming1, cjming@mail.nwpu.edu.cn, Li, Ni1, lini@nwpu.edu.cn, Dong, Changyin1, dongcy@nwpu.edu.cn, Tang, Chong2, chong.tang@soton.ac.uk
Source: Knowledge-Based Systems; Nov2025:Part C, Vol. 330, pN.PAG-N.PAG, 1p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 189882875
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=189882875
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.knosys.2025.114534
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      – Code: eng
        Text: English
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      – TitleFull: Enhancing multi-agent reinforcement learning via world model assisted single-agent population policies in multi-UAV cooperative-competitive scenario.
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            NameFull: Li, Ni
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              Text: Nov2025:Part C
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              Y: 2025
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              Value: 330
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