Demonstration-based Policy Learning

Demonstration-Based Policy Learning is a machine learning framework that acquires a policy through the observation of expert demonstrations, distinguished by its reliance on behavioral examples rather than explicit reward signals or environment interactions.

178

Publications

7.4K

Citations

513

Authors

149

Institutions

Publications per year

2017–2026

120

Variants

Learning From Demonstration

Authors

513

  1. SC
    Sonia Chernova

    Georgia Institute of Technology, United States

    11 publications · 1K citations

    11

    h-index

  2. SL
    Sergey Levine

    University of California, Berkeley, United States

    9 publications · 642 citations

    9

    h-index

  3. ME
    Matthew E. Taylor

    Washington State University, United States

    7 publications · 454 citations

    7

    h-index

  4. KG
    Ken Goldberg

    University of California, Berkeley, United States

    5 publications · 197 citations

    5

    h-index

  5. CL
    Chen Lv

    Nanyang Technological University, Singapore

    5 publications · 314 citations

    5

    h-index

1–5 of 513

Venues

  1. arXiv (Cornell University)

    Repository · 37 publications · 1.2K citations

    18

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 8 publications · 266 citations

    8

    h-index

  3. IEEE Robotics and Automation Letters

    6 publications · 162 citations

    6

    h-index

  4. Autonomous Robots

    4 publications · 157 citations

    5

    h-index

  5. International Journal of Social Robotics

    3 publications · 290 citations

    3

    h-index

1–5