Deterministic Policy Learning

Deterministic Policy Learning is a method in reinforcement learning whereby the policy, as a mapping from states to actions, is learned to output a single, specific action for each state without stochastic variation.

301

Publications

30.5K

Citations

1K

Authors

326

Institutions

Publications per year

2017–2026

265

Authors

1K

  1. CW
    Cong Wang

    South China University of Technology, China

    13 publications · 449 citations

    13

    h-index

  2. WZ
    Wei Zeng

    Longyan University, China

    11 publications · 550 citations

    11

    h-index

  3. NH
    Nicolas Heess

    Google (United States), United States

    5 publications · 14.6K citations

    5

    h-index

  4. CY
    Chengzhi Yuan

    University of Rhode Island, United States

    4 publications · 132 citations

    4

    h-index

  5. SW
    Shimon Whiteson

    University of Oxford, United Kingdom

    4 publications · 271 citations

    4

    h-index

1–5 of 1K

Venues

  1. arXiv (Cornell University)

    Repository · 25 publications · 15.9K citations

    17

    h-index

  2. IEEE Access

    12 publications · 583 citations

    12

    h-index

  3. IEEE Internet of Things Journal

    8 publications · 994 citations

    8

    h-index

  4. Neurocomputing

    7 publications · 214 citations

    7

    h-index

  5. Neural Networks

    7 publications · 199 citations

    7

    h-index

1–5