Reward Models
Reward Models is a computational framework within reinforcement learning that formalizes the specification of desired agent behavior by defining the scalar feedback signal, distinguishing it from alternative decision-making mechanisms through its focus on learning or modeling the reward function from human preferences or environmental interactions.
232
Publications
15.7K
Citations
640
Authors
263
Institutions
Publications per year
2017–2026
63
232
Temporal Difference Models and Reward-Related Learning in the Human Brain
John P. O’Doherty, Peter Dayan, Karl Friston et al. · Neuron · 2003 · 1.5K citations · Full text
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Duke University
Durham, United States
13 h-index
27 publications
1.9K citations
University of Illinois Urbana-Champaign
Urbana, United States
10 h-index
23 publications
934 citations
University of California, Berkeley
Berkeley, United States
8 h-index
12 publications
773 citations
Stanford University
Stanford, United States
6 h-index
8 publications
3.4K citations

University of Twente
Enschede, The Netherlands
6 h-index
13 publications
1.2K citations

Cornell University
Ithaca, United States
5 h-index
8 publications
672 citations
AT&T (United States)
Bedminster, United States
5 h-index
6 publications
371 citations
University of Washington
Seattle, United States
5 h-index
8 publications
753 citations
1–8 of 263
Repository · 15 publications · 1.1K citations
13
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6 publications · 509 citations
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5 publications · 205 citations
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4 publications · 373 citations
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3 publications · 174 citations
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