Policy Iteration Optimality

Policy Iteration Optimality is a theoretical construct in reinforcement learning that characterizes the condition under which a policy iteration algorithm converges to a globally optimal policy by guaranteeing that each iterative policy improvement step strictly dominates the previous policy in terms of long-term value maximization.

38

Publications

2.3K

Citations

90

Authors

65

Institutions

Publications per year

2017–2026

11

Authors

90

  1. QW
    Qinglai Wei

    Chinese Academy of Sciences, China

    3 publications · 167 citations

    3

    h-index

  2. XC
    Xi‐Ren Cao

    Hong Kong University of Science and Technology, Hong Kong SAR China

    3 publications · 74 citations

    3

    h-index

  3. DL
    Derong Liu

    Chinese Academy of Sciences, China

    2 publications · 99 citations

    2

    h-index

  4. CB
    Christian Bender

    Technische Universität Braunschweig, Germany

    2 publications · 34 citations

    2

    h-index

  5. JS
    John Schoenmakers

    Weierstrass Institute for Applied Analysis and Stochastics, Germany

    2 publications · 34 citations

    2

    h-index

1–5 of 90

Venues

  1. Automatica

    4 publications · 1K citations

    3

    h-index

  2. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 2 publications · 48 citations

    2

    h-index

  3. Journal of Applied Probability

    JAP · 2 publications · 57 citations

    2

    h-index

  4. 2

    h-index

  5. IEEE Transactions on Automatic Control

    2 publications · 35 citations

    2

    h-index

1–5