Iterative Policy Learning
Iterative Policy Learning is a methodological framework within public sector experimentation that systematically refines policy interventions through successive cycles of design, implementation, evaluation, and adaptation based on empirical feedback.
49
Publications
3.3K
Citations
133
Authors
67
Institutions
Publications per year
2017–2026
17
49
An iterative learning control law for dynamical systems
Toshiharu Sugie, Toshiro Ono · Automatica · 1991 · 213 citations
A Theoretical Analysis of Deep Q-Learning
Zhuoran Yang, Yuchen Xie, Zhaoran Wang · Learning for Dynamics and Control · 2020 · 194 citations
On iterative learning algorithms for the formation control of nonlinear multi-agent systems
Deyuan Meng, Yingmin Jia, Junping Du et al. · Automatica · 2013 · 166 citations
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67
National University of Singapore
Singapore, Singapore
3 h-index
4 publications
267 citations
Carnegie Mellon University
Pittsburgh, United States
3 h-index
7 publications
258 citations
Osaka Prefecture University
Sakai, Japan
2 h-index
3 publications
235 citations
ETH Zurich
Zurich, Switzerland
2 h-index
7 publications
522 citations
Harvard University
Cambridge, United States
2 h-index
2 publications
60 citations
Institute of Development Studies
Brighton, United Kingdom
1 h-index
1 publications
13 citations
Beijing Academy of Artificial Intelligence
Beijing, China
1 h-index
5 publications
560 citations

McMaster University
Hamilton, Canada
1 h-index
2 publications
26 citations
1–8 of 67
4 publications · 462 citations
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Repository · 2 publications · 145 citations
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2 publications · 25 citations
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1 publications · 18 citations
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1 publications · 98 citations
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