Classical Policy Iteration
Classical Policy Iteration is a dynamic programming algorithm in reinforcement learning that iteratively alternates between policy evaluation and policy improvement steps to converge to an optimal policy for a Markov decision process.
12
Publications
1.1K
Citations
29
Authors
17
Institutions
Publications per year
2017–2026
3
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1–5 of 12
29
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Osaka Research Institute of Industrial Science and Technology, Japan
2 publications · 158 citations
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1–5 of 29
17
University of Southern California
Los Angeles, United States
2 h-index
3 publications
91 citations

Tel Aviv University
Tel Aviv, Israel
1 h-index
1 publications
10 citations

University of Leeds
Leeds, United Kingdom
1 h-index
1 publications
102 citations
Indian Institute of Technology Delhi
New Delhi, India
1 h-index
1 publications
16 citations

Sapienza University of Rome
Rome, Italy
1 h-index
1 publications
102 citations
University of California, Berkeley
Berkeley, United States
1 h-index
1 publications
59 citations
Massachusetts Institute of Technology
Cambridge, United States
1 h-index
1 publications
278 citations
Robotics Research (United States)
Cincinnati, United States
1 h-index
1 publications
232 citations
1–8 of 17
1 publications · 49 citations
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1 publications · 232 citations
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1 publications · 102 citations
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1 publications · 59 citations
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1 publications · 16 citations
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