Policy Iteration

Policy Iteration is a dynamic programming algorithm in the context of Markov Decision Processes that iteratively improves a policy by alternating between policy evaluation, which computes the value function for the current policy, and policy improvement, which updates the policy using the greedy action-selection rule with respect to the computed value function.

2.2K

Publications

248.2K

Citations

4.4K

Authors

1.2K

Institutions

Publications per year

2017–2026

972

Authors

4.4K

  1. FL
    Frank L. Lewis

    The University of Texas at Arlington, United States

    46 publications · 5.9K citations

    31

    h-index

  2. RM
    Rémi Munos

    Google (United States), United States

    25 publications · 2.2K citations

    24

    h-index

  3. DL
    Derong Liu

    Chinese Academy of Sciences, China

    27 publications · 4.1K citations

    24

    h-index

  4. SL
    Sergey Levine

    University of California, Berkeley, United States

    24 publications · 7.2K citations

    19

    h-index

  5. RS
    Richard S. Sutton

    University of Alberta, Canada

    23 publications · 36.6K citations

    18

    h-index

1–5 of 4.4K

Venues

  1. arXiv (Cornell University)

    Repository · 268 publications · 28.9K citations

    54

    h-index

  2. 35

    h-index

  3. IEEE Transactions on Automatic Control

    36 publications · 2.7K citations

    27

    h-index

  4. Machine Learning

    35 publications · 11.8K citations

    24

    h-index

  5. IEEE Transactions on Cybernetics

    31 publications · 2.4K citations

    24

    h-index

1–5