Reinforcement Learning For Offloading

Reinforcement Learning for Offloading is a computational framework within task offloading that employs reinforcement learning methods to dynamically optimize offloading decisions by learning optimal policies through agent-environment interactions.

377

Publications

29.6K

Citations

1.3K

Authors

459

Institutions

Publications per year

2017–2026

368

Authors

1.3K

  1. DN
    Dusit Niyato

    Nanyang Technological University, Singapore

    9 publications · 2.9K citations

    9

    h-index

  2. GM
    Geyong Min

    University of Exeter, United Kingdom

    7 publications · 926 citations

    7

    h-index

  3. YJ
    Yusheng Ji

    National Institute of Informatics, Japan

    7 publications · 1.1K citations

    7

    h-index

  4. SG
    Shimin Gong

    Sun Yat-sen University, China

    6 publications · 2.2K citations

    6

    h-index

  5. ZL
    Zhi Liu

    University of Electro-Communications, Japan

    6 publications · 445 citations

    6

    h-index

1–5 of 1.3K

Venues

  1. IEEE Internet of Things Journal

    41 publications · 3.7K citations

    35

    h-index

  2. IEEE Transactions on Vehicular Technology

    25 publications · 3K citations

    24

    h-index

  3. IEEE Access

    18 publications · 1K citations

    18

    h-index

  4. IEEE Transactions on Mobile Computing

    TMCCI · 15 publications · 2.4K citations

    15

    h-index

  5. IEEE Journal on Selected Areas in Communications

    13 publications · 2.2K citations

    13

    h-index

1–5