Interactive Policy Refinement
Interactive Policy Refinement is a machine learning framework within human-robot interaction that enables autonomous agents to iteratively improve their decision-making policies through direct, real-time feedback from human users, distinguishing it from passive or offline policy optimization methods by emphasizing bidirectional interaction and adaptive learning during task execution.
3
Publications
139
Citations
15
Authors
6
Institutions
Publications per year
2017–2026
2
3
Reinforcement Learning from Imperfect Demonstrations
Yang Gao, Huazhe, Ji Lin et al. · arXiv (Cornell University) · 2018 · 99 citations · Full text
Usable Policy Template Authoring for Iterative Policy Refinement
Maritza Johnson, John Karat, Clare-Marie Karat et al. · 2010 · 25 citations
1–5 of 3
15
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1–5 of 15
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IBM Research - Thomas J. Watson Research Center
Yorktown Heights, United States
1 h-index
3 publications
75 citations
Ben-Gurion University of the Negev
Beersheba, Israel
1 h-index
2 publications
30 citations
Columbia University
New York, United States
1 h-index
1 publications
25 citations
Microsoft (United States)
Redmond, United States
1 h-index
2 publications
30 citations
IBM (United States)
Armonk, United States
1 h-index
3 publications
75 citations
University of Oxford
Oxford, United Kingdom
1 h-index
1 publications
15 citations

Delft University of Technology
Delft, The Netherlands
1 publications
12 citations
University of Chile
Santiago, Chile
1 publications
12 citations
1–8 of 6
Repository · 1 publications · 99 citations
1
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Conference · 1 publications · 15 citations
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SPAR · Book series · 1 publications · 12 citations
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