Decentralized Execution
Decentralized Execution is a control paradigm in multi-robot learning where each robot independently computes its actions based on local observations and policies without centralized coordination, thereby enabling scalable and robust task execution through autonomous decision-making.
213
Publications
10.7K
Citations
746
Authors
327
Institutions
Publications per year
2017–2026
146
Parents
213
Learning to Communicate with Deep Multi-Agent Reinforcement Learning
Jakob Foerster, Yannis Assael, Nando de Freitas et al. · arXiv (Cornell University) · 2016 · 870 citations · Full text
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327
University of Oxford
Oxford, United Kingdom
5 h-index
14 publications
1.3K citations

Chinese Academy of Sciences
Beijing, China
5 h-index
16 publications
538 citations

Sun Yat-sen University
Guangzhou, China
5 h-index
10 publications
845 citations

University of Milan
Milan, Italy
4 h-index
4 publications
141 citations
University of Chinese Academy of Sciences
Beijing, China
4 h-index
11 publications
344 citations
Nanyang Technological University
Singapore, Singapore
4 h-index
9 publications
448 citations
Carnegie Mellon University
Pittsburgh, United States
4 h-index
9 publications
248 citations

Centre National de la Recherche Scientifique
Paris, France
4 h-index
9 publications
754 citations
1–8 of 327
Repository · 23 publications · 2.2K citations
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4 publications · 556 citations
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4 publications · 128 citations
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4 publications · 379 citations
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3 publications · 230 citations
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