Batch Policy Iteration

Batch Policy Iteration is a reinforcement learning algorithm that, within the class of policy iteration methods, updates the policy using a fixed batch of data collected from prior interactions with the environment, thereby decoupling the data collection process from the policy improvement steps.

7

Publications

234

Citations

22

Authors

19

Institutions

Publications per year

2017–2026

4

Authors

22

  1. CS
    Csaba Szepesvári

    University of Alberta, Canada

    2 publications · 41 citations

    2

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  2. YJ
    Yaochu Jin

    University of Surrey, United Kingdom

    1 publications · 25 citations

    1

    h-index

  3. PS
    Philip S. Thomas

    University of Massachusetts Amherst, United States

    1 publications · 96 citations

    1

    h-index

  4. AA
    András Antos

    Hungarian Academy of Sciences, Hungary

    1 publications · 30 citations

    1

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  5. HA
    Hoai An Le Thi

    Institut National des Sciences Appliquées Rouen Normandie, France

    1 publications · 11 citations

    1

    h-index

1–5 of 22

Venues

  1. 1

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  2. Knowledge-Based Systems

    1 publications · 25 citations

    1

    h-index

  3. Journal of Global Optimization

    1 publications · 11 citations

    1

    h-index

  4. International Conference on Machine Learning

    Conference · 1 publications · 96 citations

    1

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  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 11 citations

    1

    h-index

1–5