Preference-based Reward Learning
Preference-Based Reward Learning is a reward learning framework that infers a reward function by modeling preferences expressed over pairs of trajectories or behaviors, distinguishing itself from other reward learning methods through its reliance on comparative human feedback rather than absolute or numerical rewards.
19
Publications
929
Citations
66
Authors
25
Institutions
Publications per year
2017–2026
15
Variants
Pbrl
Parents
19
Active Preference-Based Learning of Reward Functions
Dorsa Sadigh, Anca D. Dragan, S. Shankar Sastry et al. · 2017 · 260 citations · Full text
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Stanford University
Stanford, United States
2 h-index
5 publications
206 citations
University of Colorado Anschutz Medical Campus
Aurora, United States
1 h-index
2 publications
22 citations
Corvallis Environmental Center
Corvallis, United States
1 h-index
3 publications
162 citations
The University of Texas at Austin
Austin, United States
1 h-index
2 publications
30 citations
Nanyang Technological University
Singapore, Singapore
1 h-index
4 publications
100 citations
University of Washington
Seattle, United States
1 h-index
1 publications
23 citations

St. John's University
New York, United States
1 h-index
1 publications
23 citations
The University of Texas MD Anderson Cancer Center
Houston, United States
1 h-index
3 publications
267 citations
1–8 of 25
Repository · 4 publications · 141 citations
4
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1 publications · 23 citations
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THRI · 1 publications · 12 citations
1
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1 publications · 89 citations
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1 publications · 11 citations
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