Action-advantage Function

The Action-Advantage Function is a value function in reinforcement learning that quantifies the relative benefit of selecting a specific action over a baseline policy by measuring the difference between the action-value function and the state-value function under a given policy.

18

Publications

7.2K

Citations

61

Authors

30

Institutions

Publications per year

2017–2026

15

Authors

61

  1. CZ
    Chongjie Zhang

    Tsinghua University, China

    2 publications · 64 citations

    2

    h-index

  2. RW
    Rui Wang

    National University of Defense Technology, China

    2 publications · 64 citations

    2

    h-index

  3. ZX
    Zehui Xiong

    Nanyang Technological University, Singapore

    1 publications · 76 citations

    1

    h-index

  4. RS
    Richard S. Sutton

    University of Alberta, Canada

    1 publications · 5K citations

    1

    h-index

  5. CH
    Changqiang Huang

    Air Force Engineering University, China

    1 publications · 19 citations

    1

    h-index

1–5 of 61

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 1.9K citations

    6

    h-index

  2. Sensors

    1 publications · 21 citations

    1

    h-index

  3. Frontiers in Neurorobotics

    1 publications · 19 citations

    1

    h-index

  4. Adelaide Research & Scholarship (AR&S) (University of Adelaide)

    Repository · 1 publications · 15 citations

    1

    h-index

  5. 1

    h-index

1–5