Reinforcement Learning Transfer

Reinforcement Learning Transfer is a transfer learning methodology that leverages knowledge acquired in one or more source reinforcement learning tasks to improve learning efficiency, performance, or generalization in a distinct but related target reinforcement learning task.

561

Publications

34.7K

Citations

2K

Authors

546

Institutions

Publications per year

2017–2026

426

Authors

2K

  1. PS
    Peter Stone

    The University of Texas at Austin, United States

    22 publications · 3.3K citations

    20

    h-index

  2. ME
    Matthew E. Taylor

    Washington State University, United States

    20 publications · 3.2K citations

    19

    h-index

  3. SL
    Sergey Levine

    University of California, Berkeley, United States

    8 publications · 533 citations

    8

    h-index

  4. PA
    Pieter Abbeel

    University of California, Berkeley, United States

    7 publications · 477 citations

    7

    h-index

  5. AH
    Anna Helena Reali Costa

    Universidade de São Paulo, Brazil

    7 publications · 486 citations

    7

    h-index

1–5 of 2K

Venues

  1. arXiv (Cornell University)

    Repository · 103 publications · 5K citations

    33

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 12 publications · 751 citations

    11

    h-index

  3. IEEE Robotics and Automation Letters

    10 publications · 333 citations

    9

    h-index

  4. Knowledge-Based Systems

    7 publications · 270 citations

    7

    h-index

  5. The International Journal of Robotics Research

    IJRR · 5 publications · 2.2K citations

    5

    h-index

1–5