Offline Imitation Learning

Offline Imitation Learning is a method in imitation learning that leverages a fixed, pre-collected dataset of expert demonstrations to train a policy without requiring further interaction with the environment.

24

Publications

1.1K

Citations

114

Authors

38

Institutions

Publications per year

2017–2026

22

Authors

114

  1. CF
    Chelsea Finn

    University of California, Berkeley, United States

    2 publications · 51 citations

    2

    h-index

  2. MJ
    Moo Jin Kim

    Stanford University, United States

    2 publications · 51 citations

    2

    h-index

  3. DX
    Danfei Xu

    Stanford University, United States

    2 publications · 103 citations

    2

    h-index

  4. LF
    Li Fei-Fei

    Stanford University, United States

    2 publications · 103 citations

    2

    h-index

  5. AM
    Ajay Mandlekar

    Stanford University, United States

    2 publications · 103 citations

    2

    h-index

1–5 of 114

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 174 citations

    5

    h-index

  2. 1

    h-index

  3. IEEE Transactions on Mobile Computing

    TMCCI · 1 publications · 54 citations

    1

    h-index

  4. IEEE Transactions on Information Theory

    1 publications · 55 citations

    1

    h-index

  5. Journal of Aerospace Information Systems

    JAIS · 1 publications · 16 citations

    1

    h-index

1–5