High-dimensional State Spaces

High-Dimensional State Spaces is a state space in reinforcement learning characterized by a large number of state variables or features, which distinguishes it from low-dimensional state spaces by exacerbating computational and sampling challenges in value function approximation and policy optimization.

856

Publications

53.3K

Citations

2.3K

Authors

687

Institutions

Publications per year

2017–2026

353

Authors

2.3K

  1. PA
    Pieter Abbeel

    University of California, Berkeley, United States

    10 publications · 888 citations

    10

    h-index

  2. NH
    Nicolas Heess

    Google (United States), United States

    9 publications · 2K citations

    9

    h-index

  3. JP
    Jan Peters

    Technische Universität Darmstadt, Germany

    9 publications · 351 citations

    9

    h-index

  4. LP
    Leslie Pack Kaelbling

    Massachusetts Institute of Technology, United States

    8 publications · 407 citations

    8

    h-index

  5. MW
    Mengdi Wang

    Princeton University, United States

    7 publications · 226 citations

    7

    h-index

1–5 of 2.3K

Venues

  1. arXiv (Cornell University)

    Repository · 129 publications · 5.9K citations

    38

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 12 publications · 639 citations

    11

    h-index

  3. IEEE Robotics and Automation Letters

    9 publications · 473 citations

    9

    h-index

  4. IEEE Transactions on Signal Processing

    8 publications · 612 citations

    8

    h-index

  5. The International Journal of Robotics Research

    IJRR · 7 publications · 4K citations

    7

    h-index

1–5