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
Parents
16
A new asynchronous reinforcement learning algorithm based on improved parallel PSO
Shifei Ding, Wei Du, Xingyu Zhao et al. · Applied Intelligence · 2019 · 46 citations
1–5 of 16
31
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Massachusetts Institute of Technology, United States
3 publications · 85 citations
3
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3
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1–5 of 31
21
Massachusetts Institute of Technology
Cambridge, United States
5 h-index
6 publications
153 citations
China University of Mining and Technology
Xuzhou, China
4 h-index
14 publications
482 citations

Shandong Normal University
Jinan, China
4 h-index
4 publications
139 citations

University of Helsinki
Helsinki, Finland
2 h-index
2 publications
46 citations
University of Colorado Boulder
Boulder, United States
2 h-index
2 publications
348 citations
Georgia Institute of Technology
Atlanta, United States
2 h-index
2 publications
348 citations
Decision Systems (United States)
Northbrook, United States
2 h-index
3 publications
97 citations
University of Wisconsin–Madison
Madison, United States
1 h-index
1 publications
27 citations
1–8 of 21
5 publications · 155 citations
5
h-index
1 publications · 19 citations
1
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1 publications · 238 citations
1
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1 publications · 299 citations
1
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1 publications · 22 citations
1
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1–5