Stationary Policy Optimization

Stationary Policy Optimization is a methodological framework within Infinite-Horizon Markov Decision Processes that systematically identifies time-invariant policies maximizing long-term expected reward by restricting the search space to stationary decision rules.

96

Publications

5.3K

Citations

164

Authors

93

Institutions

Publications per year

2017–2026

17

Authors

164

  1. RC
    Rolando Cavazos–Cadena

    Universidad Autónoma Agraria Antonio Narro, Mexico

    5 publications · 106 citations

    5

    h-index

  2. HC
    He Chen

    The University of Sydney, Australia

    4 publications · 140 citations

    4

    h-index

  3. QW
    Qian Wang

    Chinese University of Hong Kong, Hong Kong SAR China

    4 publications · 140 citations

    4

    h-index

  4. BV
    Branka Vucetic

    The University of Sydney, Australia

    3 publications · 124 citations

    3

    h-index

  5. LI
    Linn I. Sennott

    Illinois State University, United States

    3 publications · 163 citations

    3

    h-index

1–5 of 164

Venues

  1. Operations Research

    8 publications · 1.1K citations

    8

    h-index

  2. Management Science

    8 publications · 1K citations

    8

    h-index

  3. Journal of Applied Probability

    JAP · 7 publications · 308 citations

    7

    h-index

  4. Mathematics of Operations Research

    6 publications · 301 citations

    6

    h-index

  5. IEEE Transactions on Automatic Control

    5 publications · 239 citations

    5

    h-index

1–5