Goal-conditioned Learning

Goal-Conditioned Learning is a reinforcement learning framework that belongs to the class of goal-directed learning methods, distinguished by its conditioning of policy learning on achieving specific future states or configurations, thereby enabling generalized skill acquisition through goal-space conditioning.

97

Publications

4.9K

Citations

326

Authors

110

Institutions

Publications per year

2017–2026

83

Authors

326

  1. SL
    Sergey Levine

    University of California, Berkeley, United States

    15 publications · 863 citations

    13

    h-index

  2. SB
    Shikhar Bahl

    Carnegie Mellon University, United States

    4 publications · 477 citations

    4

    h-index

  3. VH
    Vitchyr H. Pong

    University of California, Berkeley, United States

    4 publications · 430 citations

    4

    h-index

  4. CC
    Cédric Colas

    Institut Systèmes Intelligents et de Robotique, France

    3 publications · 88 citations

    3

    h-index

  5. CF
    Chelsea Finn

    University of California, Berkeley, United States

    2 publications · 39 citations

    3

    h-index

1–5 of 326

Venues

  1. arXiv (Cornell University)

    Repository · 27 publications · 1.1K citations

    18

    h-index

  2. IEEE Robotics and Automation Letters

    7 publications · 301 citations

    10

    h-index

  3. Journal of Experimental Psychology

    2 publications · 112 citations

    2

    h-index

  4. American Educational Research Journal

    AERJ · 2 publications · 709 citations

    2

    h-index

  5. International Conference on Learning Representations

    Conference · 2 publications · 30 citations

    2

    h-index

1–5