Learning From Human Feedback

Learning From Human Feedback is a computational learning paradigm within Human-Robot Interaction and Imitation Learning in which an agent acquires skills or refines behavior through direct supervision, evaluation, or corrective input provided by humans, distinguishing it from other learning paradigms by its reliance on explicit, real-time, or iterative feedback from human sources to guide or shape the learning process.

299

Publications

20.9K

Citations

1.2K

Authors

289

Institutions

Publications per year

2017–2026

213

Authors

1.2K

  1. DA
    Dario Amodei

    Stanford University, United States

    9 publications · 1.6K citations

    9

    h-index

  2. EP
    Ethan Perez

    Rice University, United States

    8 publications · 703 citations

    9

    h-index

  3. WB
    W. Bradley Knox

    The University of Texas at Austin, United States

    9 publications · 431 citations

    9

    h-index

  4. ME
    Matthew E. Taylor

    Washington State University, United States

    8 publications · 481 citations

    9

    h-index

  5. AL
    Andrea L. Thomaz

    Georgia Institute of Technology, United States

    7 publications · 850 citations

    7

    h-index

1–5 of 1.2K

Venues

  1. arXiv (Cornell University)

    Repository · 71 publications · 8.7K citations

    35

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 7 publications · 302 citations

    7

    h-index

  3. Autonomous Agents and Multi-Agent Systems

    4 publications · 149 citations

    4

    h-index

  4. 4

    h-index

  5. IEEE Transactions on Human-Machine Systems

    3 publications · 178 citations

    3

    h-index

1–5