Q-function Recursion

Q-Function Recursion is a recursive formulation in reinforcement learning that belongs to the class of value functions, specifically distinguished by its definition of the expected cumulative reward for taking a particular action in a given state and thereafter following a fixed policy, with subsequent values computed through iterative application of the Bellman optimality or expectation operator applied to action-value pairs.

7

Publications

491

Citations

7

Authors

3

Institutions

Publications per year

2017–2026

0

Authors

7

  1. LB
    L. Brennan

    A. Alikhanyan National Laboratory, Armenia

    2 publications · 78 citations

    2

    h-index

  2. IR
    I.S. Reed

    University of Southern California, United States

    2 publications · 78 citations

    2

    h-index

  3. NV
    Nikos Vlassis

    University of Amsterdam, Netherlands

    1 publications · 25 citations

    1

    h-index

  4. SP
    Steen Parl

    1 publications · 58 citations

    1

    h-index

  5. FA
    Frans A. Oliehoek

    University of Amsterdam, Netherlands

    1 publications · 25 citations

    1

    h-index

1–5 of 7

Institutions

3

  1. University of Amsterdam

    Amsterdam, The Netherlands

    2 h-index

    3 publications

    297 citations

  2. Technical University of Crete

    Chania, Greece

    1 h-index

    1 publications

    247 citations

  3. INESC TEC

    Porto, Portugal

    1 h-index

    1 publications

    247 citations

1–3 of 3

Venues

  1. IEEE Transactions on Information Theory

    3 publications · 126 citations

    3

    h-index

  2. Journal of Artificial Intelligence Research

    JAIR · 1 publications · 247 citations

    1

    h-index

  3. Defense Technical Information Center (DTIC)

    Repository · 1 publications · 39 citations

    1

    h-index

  4. 1

    h-index

1–4