Reward Normalization
Reward Normalization is a method in reward design that scales and adjusts reward signals to a consistent range or statistical distribution, enabling stable and efficient learning in reinforcement learning systems by mitigating the impact of reward magnitude variability.
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477
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Authors
25
Institutions
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2017–2026
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Learning values across many orders of magnitude
Hado van Hasselt, Arthur Guez, Matteo Hessel et al. · arXiv (Cornell University) · 2016 · 87 citations · Full text
Free choice shapes normalized value signals in medial orbitofrontal cortex
Hiroshi Yamada, Kenway Louie, Agnieszka Tymula et al. · Nature Communications · 2018 · 86 citations · Full text
Qingqing Sun, Hong Chen, Ruyin Long et al. · European Journal of Operational Research · 2022 · 67 citations
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New York University
New York, United States
3 h-index
8 publications
581 citations

UNSW Sydney
Sydney, Australia
1 h-index
1 publications
86 citations

Huazhong University of Science and Technology
Wuhan, China
1 h-index
4 publications
88 citations
École Normale Supérieure
Bujumbura, Burundi
1 h-index
2 publications
64 citations
Jiangnan University
Wuxi, China
1 h-index
2 publications
134 citations
East China University of Science and Technology
Shanghai, China
1 h-index
1 publications
22 citations
Northwestern University
Evanston, United States
1 h-index
5 publications
105 citations
University of Washington
Seattle, United States
1 h-index
1 publications
21 citations
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2 publications · 158 citations
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Repository · 2 publications · 98 citations
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1 publications · 67 citations
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1 publications · 21 citations
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1 publications · 32 citations
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