Mean Field Learning
Mean Field Learning is a computational framework within Multi-Agent Robot Learning that approximates each agent's interaction with a large population by modeling the collective behavior of all agents as a continuous mean field, thereby simplifying decentralized decision-making in high-dimensional multi-agent systems.
139
Publications
7.7K
Citations
309
Authors
170
Institutions
Publications per year
2017–2026
86
Parents
139
Semantic Image Segmentation via Deep Parsing Network
Ziwei Liu, Xiaoxiao Li, Ping Luo et al. · 2015 · 606 citations
Mean Field Games Models—A Brief Survey
Diogo A. Gomes, João Saúde · Dynamic Games and Applications · 2013 · 363 citations
1–5 of 139
309
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h-index
6
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6
h-index
Centre de Recherche en Mathématiques de la Décision, France
6 publications · 132 citations
6
h-index
6
h-index
1–5 of 309
170
Princeton University
Princeton, United States
12 h-index
15 publications
539 citations
University of Illinois Urbana-Champaign
Urbana, United States
6 h-index
11 publications
216 citations
University of Houston
Houston, United States
6 h-index
10 publications
410 citations
University of California, Berkeley
Berkeley, United States
5 h-index
7 publications
177 citations
Stanford University
Stanford, United States
5 h-index
9 publications
2K citations

Lund University
Lund, Sweden
4 h-index
8 publications
632 citations
University of Oxford
Oxford, United Kingdom
4 h-index
5 publications
134 citations
Google (United States)
Mountain View, United States
4 h-index
8 publications
238 citations
1–8 of 170
Repository · 24 publications · 1K citations
16
h-index
Conference · 6 publications · 274 citations
6
h-index
4 publications · 703 citations
4
h-index
Conference · 3 publications · 200 citations
3
h-index
3 publications · 92 citations
3
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