Constrained Markov Decision Processes

Constrained Markov Decision Processes is a class of Markov decision process models for sequential stochastic control in which an agent optimizes an expected performance criterion (such as discounted or average reward) subject to additional constraints expressed as bounds on expected cumulative costs or risks along controlled trajectories.

336

Publications

15.4K

Citations

770

Authors

308

Institutions

Publications per year

2017–2026

154

Authors

770

  1. EA
    Eugene A. Feinberg

    Stony Brook University, United States

    9 publications · 514 citations

    9

    h-index

  2. AS
    Adam Shwartz

    Technion – Israel Institute of Technology, Israel

    8 publications · 376 citations

    8

    h-index

  3. EA
    Eitan Altman

    Institut national de recherche en sciences et technologies du numérique, France

    8 publications · 289 citations

    8

    h-index

  4. LT
    Lang Tong

    Cornell University, United States

    7 publications · 572 citations

    7

    h-index

  5. NB
    Ness B. Shroff

    The Ohio State University, United States

    7 publications · 171 citations

    7

    h-index

1–5 of 770

Venues

  1. arXiv (Cornell University)

    Repository · 27 publications · 898 citations

    15

    h-index

  2. IEEE Transactions on Automatic Control

    13 publications · 441 citations

    12

    h-index

  3. Mathematical Methods of Operations Research

    11 publications · 352 citations

    10

    h-index

  4. IEEE Transactions on Wireless Communications

    11 publications · 670 citations

    10

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 9 publications · 417 citations

    9

    h-index

1–5