Inverse Optimal Control

Inverse Optimal Control is a method in reinforcement learning that infers the underlying reward function from expert demonstrations by identifying the cost criteria that render the observed behavior optimal.

333

Publications

17.7K

Citations

682

Authors

306

Institutions

Publications per year

2017–2026

134

Authors

682

  1. EN
    Edgar N. Sánchez

    Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Mexico

    27 publications · 730 citations

    16

    h-index

  2. AY
    Alma Y. Alanís

    Universidad de Guadalajara, Mexico

    11 publications · 228 citations

    10

    h-index

  3. FO
    Fernando Ornelas-Téllez

    Universidad Michoacana de San Nicolás de Hidalgo, Mexico

    10 publications · 353 citations

    10

    h-index

  4. JA
    J. Andrew Bagnell

    Carnegie Mellon University, United States

    8 publications · 991 citations

    8

    h-index

  5. MK
    Miroslav Krstić

    University of California San Diego, United States

    8 publications · 1.9K citations

    8

    h-index

1–5 of 682

Venues

  1. IEEE Transactions on Automatic Control

    16 publications · 2.9K citations

    15

    h-index

  2. arXiv (Cornell University)

    Repository · 12 publications · 1.1K citations

    11

    h-index

  3. International Journal of Control

    12 publications · 460 citations

    11

    h-index

  4. Automatica

    8 publications · 353 citations

    8

    h-index

  5. 8

    h-index

1–5