Reward Optimization
Reward Optimization is a methodological framework within reward governance that systematically aligns incentive structures with desired behavioral outcomes by maximizing the efficacy, fairness, and strategic coherence of rewards in multi-agent or organizational systems.
42
Publications
1.5K
Citations
139
Authors
63
Institutions
Publications per year
2017–2026
21
Parents
42
Scalable agent alignment via reward modeling: a research direction
Jan Leike, David Krueger, Tom Everitt et al. · arXiv (Cornell University) · 2018 · 125 citations · Full text
Peter I. Frazier, David Kempe, Jon Kleinberg et al. · 2014 · 107 citations
Qingqing Sun, Hong Chen, Ruyin Long et al. · European Journal of Operational Research · 2022 · 67 citations
1–5 of 42
139
2
h-index
2
h-index
2
h-index
2
h-index
2
h-index
1–5 of 139
63
Dalian University of Technology
Dalian, China
2 h-index
8 publications
206 citations
Xi'an University of Science and Technology
Xi'an, China
2 h-index
11 publications
246 citations
University of California, Berkeley
Berkeley, United States
2 h-index
5 publications
264 citations
University of Washington
Seattle, United States
2 h-index
5 publications
125 citations
Virginia Tech
Blacksburg, United States
2 h-index
3 publications
61 citations
Tsinghua University
Beijing, China
2 h-index
6 publications
335 citations
Nanyang Technological University
Singapore, Singapore
2 h-index
5 publications
101 citations

Longwood University
Farmville, United States
1 h-index
1 publications
47 citations
1–8 of 63
Repository · 4 publications · 225 citations
4
h-index
2 publications · 46 citations
2
h-index
1 publications · 11 citations
1
h-index
1 publications · 39 citations
1
h-index
1 publications · 40 citations
1
h-index
1–5