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
Parents
38
1–5 of 38
90
3
h-index
Hong Kong University of Science and Technology, Hong Kong SAR China
3 publications · 74 citations
3
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2
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2
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Weierstrass Institute for Applied Analysis and Stochastics, Germany
2 publications · 34 citations
2
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1–5 of 90
65

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
3 h-index
3 publications
74 citations

Vrije Universiteit Amsterdam
Amsterdam, The Netherlands
3 h-index
4 publications
111 citations
Shandong Institute of Automation
Jinan, China
3 h-index
8 publications
458 citations

Chinese Academy of Sciences
Beijing, China
3 h-index
8 publications
458 citations
Institute of Computational Mathematics and Mathematical Geophysics
Novosibirsk, Russia
2 h-index
2 publications
34 citations
Tsinghua University
Beijing, China
2 h-index
3 publications
91 citations
Virginia Tech
Blacksburg, United States
2 h-index
3 publications
38 citations
Technische Universität Braunschweig
Braunschweig, Germany
2 h-index
2 publications
34 citations
1–8 of 65
4 publications · 1K citations
3
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TPDS · 2 publications · 48 citations
2
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JAP · 2 publications · 57 citations
2
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2 publications · 48 citations
2
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2 publications · 35 citations
2
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