Distributed Policy Optimization
Distributed Policy Optimization is a distributed reinforcement learning framework that coordinates policy updates across multiple agent-specific learners through decentralized optimization, distinguishing itself from centralized approaches by enabling scalable, asynchronous, and communication-efficient training in multi-robot systems.
14
Publications
619
Citations
53
Authors
24
Institutions
Publications per year
2017–2026
14
Parents
14
UAV-Enabled Covert Federated Learning
Xiangwang Hou, Jingjing Wang, Chunxiao Jiang et al. · IEEE Transactions on Wireless Communications · 2023 · 121 citations
Efficient and scalable reinforcement learning for large-scale network control
Chengdong Ma, Aming Li, Yali Du et al. · Nature Machine Intelligence · 2024 · 56 citations · Full text
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Harvard University Press
Cambridge, United States
2 h-index
5 publications
184 citations
Tianjin University
Tianjin, China
2 h-index
5 publications
81 citations

University of Minnesota
Minneapolis, United States
2 h-index
3 publications
82 citations
Simon Fraser University
Burnaby, Canada
1 h-index
1 publications
36 citations
Duke University
Durham, United States
1 h-index
2 publications
46 citations
Oregon State University
Corvallis, United States
1 h-index
1 publications
36 citations

Beihang University
Beijing, China
1 h-index
1 publications
121 citations
Florida International University
Miami, United States
1 h-index
3 publications
48 citations
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Repository · 5 publications · 254 citations
5
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2 publications · 60 citations
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1 publications · 58 citations
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CACAIE · 1 publications · 29 citations
1
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1 publications · 13 citations
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