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D3QN-LMA: A memory-augmented deep reinforcement learning framework for energy-latency tradeoff optimization in mobile edge computing.

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Title: D3QN-LMA: A memory-augmented deep reinforcement learning framework for energy-latency tradeoff optimization in mobile edge computing.
Authors: Mao, Yufan1, gs.yfmao24@gzu.edu.cn, Tang, Xianghong1, xhtang@gzu.edu.cn, Lu, Jianguang1, jglu@gzu.edu.cn, Li, Haonan1, gs.hnli24@gzu.edu.cn, Wang, Chaobin1, gs.cbwang23@gzu.edu.cn
Source: Advanced Engineering Informatics; May2026, Vol. 72, pN.PAG-N.PAG, 1p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 192152194
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: D3QN-LMA: A memory-augmented deep reinforcement learning framework for energy-latency tradeoff optimization in mobile edge computing.
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=192152194
RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1016/j.aei.2026.104431
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      – Code: eng
        Text: English
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      – TitleFull: D3QN-LMA: A memory-augmented deep reinforcement learning framework for energy-latency tradeoff optimization in mobile edge computing.
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            NameFull: Mao, Yufan
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            NameFull: Tang, Xianghong
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            NameFull: Lu, Jianguang
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            NameFull: Li, Haonan
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            NameFull: Wang, Chaobin
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          Dates:
            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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              Value: 72
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            – TitleFull: Advanced Engineering Informatics
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