Continuous-state Pomdps

Continuous-State POMDPs is a type of Partially Observable Markov Decision Process in which the state space is continuous rather than discrete, requiring approximation techniques for belief representation and value function estimation.

66

Publications

4K

Citations

151

Authors

66

Institutions

Publications per year

2017–2026

18

Authors

151

  1. SS
    Scott Sanner

    University of Toronto, Canada

    4 publications · 136 citations

    4

    h-index

  2. ML
    Michael L. Littman

    Rutgers, The State University of New Jersey, United States

    4 publications · 296 citations

    4

    h-index

  3. MJ
    Mykel J. Kochenderfer

    Stanford University, United States

    4 publications · 247 citations

    4

    h-index

  4. DH
    David Hsu

    National University of Singapore, Singapore

    3 publications · 188 citations

    3

    h-index

  5. ZN
    Zachary N. Sunberg

    Stanford University, United States

    3 publications · 186 citations

    3

    h-index

1–5 of 151

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 475 citations

    6

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 4 publications · 97 citations

    4

    h-index

  3. Autonomous Agents and Multi-Agent Systems

    2 publications · 454 citations

    2

    h-index

  4. European Journal of Operational Research

    2 publications · 40 citations

    2

    h-index

  5. IEEE Transactions on Automatic Control

    2 publications · 103 citations

    2

    h-index

1–5