Asynchronous Policy Iteration

Asynchronous Policy Iteration is a variant of the policy iteration algorithm in reinforcement learning where the evaluation and improvement of the policy are performed on a per-state basis in an irregular, non-sequential order, allowing convergence without requiring full sweeps over the state space.

16

Publications

945

Citations

31

Authors

21

Institutions

Publications per year

2017–2026

7

Authors

31

  1. WJ
    Weikuan Jia

    Shandong Normal University, China

    4 publications · 139 citations

    4

    h-index

  2. SD
    Shifei Ding

    China University of Mining and Technology, China

    4 publications · 139 citations

    4

    h-index

  3. XZ
    Xingyu Zhao

    Tianjin University, China

    4 publications · 139 citations

    4

    h-index

  4. DP
    Dimitri P. Bertsekas

    Massachusetts Institute of Technology, United States

    3 publications · 85 citations

    3

    h-index

  5. HY
    Huizhen Yu

    Massachusetts Institute of Technology, United States

    3 publications · 85 citations

    3

    h-index

1–5 of 31

Venues

  1. Applied Intelligence

    5 publications · 155 citations

    5

    h-index

  2. Automatica

    1 publications · 19 citations

    1

    h-index

  3. SIAM Journal on Control and Optimization

    1 publications · 238 citations

    1

    h-index

  4. Games and Economic Behavior

    1 publications · 299 citations

    1

    h-index

  5. Omega

    1 publications · 22 citations

    1

    h-index

1–5