Classical Policy Iteration

Classical Policy Iteration is a dynamic programming algorithm in reinforcement learning that iteratively alternates between policy evaluation and policy improvement steps to converge to an optimal policy for a Markov decision process.

12

Publications

1.1K

Citations

29

Authors

17

Institutions

Publications per year

2017–2026

3

Authors

29

  1. SK
    Shigenobu Kobayashi

    Tokyo Institute of Technology, Japan

    2 publications · 158 citations

    2

    h-index

  2. HK
    Hajime Kimura

    Osaka Research Institute of Industrial Science and Technology, Japan

    2 publications · 158 citations

    2

    h-index

  3. UY
    Uri Yechiali

    Tel Aviv University, Israel

    1 publications · 10 citations

    1

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  4. FG
    Fredrik Gustafsson

    Linköping University, Sweden

    1 publications · 59 citations

    1

    h-index

  5. ML
    Matteo Leonetti

    University of Leeds, United Kingdom

    1 publications · 102 citations

    1

    h-index

1–5 of 29

Venues

  1. 1

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  2. Annual Reviews in Control

    1 publications · 232 citations

    1

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  3. Artificial Intelligence

    1 publications · 102 citations

    1

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  4. IEEE Transactions on Automatic Control

    1 publications · 59 citations

    1

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  5. IEEE Transactions on Communications

    1 publications · 16 citations

    1

    h-index

1–5