Edge Reinforcement Learning

Edge Reinforcement Learning is a machine learning framework that integrates reinforcement learning with edge computing by enabling intelligent agents to learn optimal policies through environment interactions directly on distributed edge devices, thereby minimizing latency, bandwidth usage, and privacy risks associated with cloud-centric computation.

37

Publications

1.6K

Citations

153

Authors

67

Institutions

Publications per year

2017–2026

35

Authors

153

  1. SJ
    Shi Jin

    Southeast University, China

    2 publications · 223 citations

    2

    h-index

  2. SD
    Shuiguang Deng

    Zhejiang University of Science and Technology, China

    2 publications · 180 citations

    2

    h-index

  3. FR
    F. Richard Yu

    Carleton University, Canada

    2 publications · 76 citations

    2

    h-index

  4. AK
    Anit Kumar

    Lovely Professional University, India

    2 publications · 60 citations

    2

    h-index

  5. DS
    Dhanpratap Singh

    Lovely Professional University, India

    2 publications · 60 citations

    2

    h-index

1–5 of 153

Venues

  1. IEEE Access

    3 publications · 168 citations

    3

    h-index

  2. Journal of Parallel and Distributed Computing

    2 publications · 30 citations

    2

    h-index

  3. 2

    h-index

  4. arXiv (Cornell University)

    Repository · 2 publications · 41 citations

    2

    h-index

  5. Information Processing & Management

    1 publications · 10 citations

    1

    h-index

1–5