Finite Horizon Dynamic Programming

Finite Horizon Dynamic Programming is a dynamic programming framework distinguished by a predetermined, fixed number of decision stages over which optimal policies are computed to maximize cumulative rewards within the specified temporal limit.

213

Publications

18.6K

Citations

376

Authors

189

Institutions

Publications per year

2017–2026

32

Authors

376

  1. DP
    Dimitri P. Bertsekas

    Massachusetts Institute of Technology, United States

    8 publications · 579 citations

    8

    h-index

  2. QW
    Qinglai Wei

    Chinese Academy of Sciences, China

    8 publications · 1.2K citations

    8

    h-index

  3. DL
    Derong Liu

    Chinese Academy of Sciences, China

    8 publications · 1.1K citations

    8

    h-index

  4. SZ
    Shlomo Zilberstein

    University of Massachusetts Amherst, United States

    5 publications · 728 citations

    5

    h-index

  5. RA
    Ronald A. Howard

    Stanford University, United States

    4 publications · 4.5K citations

    4

    h-index

1–5 of 376

Venues

  1. IEEE Transactions on Automatic Control

    15 publications · 629 citations

    13

    h-index

  2. Operations Research

    14 publications · 840 citations

    13

    h-index

  3. Management Science

    12 publications · 764 citations

    12

    h-index

  4. Mathematics of Operations Research

    8 publications · 887 citations

    8

    h-index

  5. Journal of the Operational Research Society

    OR · 7 publications · 805 citations

    7

    h-index

1–5