Optimistic Policy Iteration

Optimistic Policy Iteration is a variant of policy iteration in reinforcement learning where the policy evaluation step is truncated after a single iteration, allowing the algorithm to proceed to policy improvement based on an incomplete but optimistic estimate of the value function, thereby accelerating convergence in certain settings.

20

Publications

576

Citations

42

Authors

29

Institutions

Publications per year

2017–2026

10

Variants

Modified Policy Iteration

Authors

42

  1. ZY
    Zhuoran Yang

    Princeton University, United States

    4 publications · 144 citations

    4

    h-index

  2. CJ
    Chi Jin

    University of California, Berkeley, United States

    3 publications · 118 citations

    3

    h-index

  3. DP
    Daniel Polani

    University of Hertfordshire, United Kingdom

    2 publications · 49 citations

    2

    h-index

  4. ZJ
    Zhong‐Ping Jiang

    New York University, United States

    2 publications · 84 citations

    2

    h-index

  5. ZW
    Zhaoran Wang

    Princeton University, United States

    2 publications · 103 citations

    2

    h-index

1–5 of 42

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 68 citations

    4

    h-index

  2. 1

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 36 citations

    1

    h-index

  4. IEEE Transactions on Automatic Control

    1 publications · 48 citations

    1

    h-index

  5. Computers & Mathematics with Applications

    1 publications · 18 citations

    1

    h-index

1–5