Multichain Markov Decision Processes

Multichain Markov Decision Processes is a class of Markov decision processes characterized by the presence of multiple recurrent classes under deterministic stationary policies, where the transition structure allows for several closed communicating sets of states and transient states, distinguishing it from unichain processes by enabling non-uniform ergodic behavior across policies.

22

Publications

987

Citations

29

Authors

26

Institutions

Publications per year

2017–2026

1

Variants

Multichain Mdps

Authors

29

  1. AF
    Awi Federgruen

    Columbia University, United States

    3 publications · 103 citations

    3

    h-index

  2. PJ
    Paul J. Schweitzer

    University of Rochester, United States

    3 publications · 103 citations

    3

    h-index

  3. AH
    Arie Hordijk

    Leiden University, Netherlands

    3 publications · 161 citations

    3

    h-index

  4. KW
    Keith W. Ross

    New York University, United States

    2 publications · 120 citations

    2

    h-index

  5. MB
    Melike Baykal‐Gürsoy

    Rutgers, The State University of New Jersey, United States

    2 publications · 53 citations

    2

    h-index

1–5 of 29

Venues

  1. Mathematics of Operations Research

    7 publications · 351 citations

    7

    h-index

  2. SIAM Journal on Applied Mathematics

    2 publications · 245 citations

    2

    h-index

  3. IEEE Transactions on Automatic Control

    2 publications · 52 citations

    2

    h-index

  4. Automatica

    1 publications · 19 citations

    1

    h-index

  5. Journal of Futures Markets

    1 publications · 18 citations

    1

    h-index

1–5