Edge Reinforcement Learning
Edge Reinforcement Learning is a machine learning framework that integrates reinforcement learning with edge computing by enabling intelligent agents to learn optimal policies through environment interactions directly on distributed edge devices, thereby minimizing latency, bandwidth usage, and privacy risks associated with cloud-centric computation.
37
Publications
1.6K
Citations
153
Authors
67
Institutions
Publications per year
2017–2026
35
Parents
37
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153
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67
Peng Cheng Laboratory
Shenzhen, China
3 h-index
7 publications
118 citations
Tsinghua University
Beijing, China
3 h-index
10 publications
302 citations

Beijing University of Posts and Telecommunications
Beijing, China
2 h-index
3 publications
112 citations

Lovely Professional University
Phagwāra, India
2 h-index
4 publications
120 citations

Chinese Academy of Sciences
Beijing, China
2 h-index
8 publications
463 citations

The University of Sydney
Sydney, Australia
2 h-index
4 publications
313 citations
Zhejiang University of Science and Technology
Hangzhou, China
2 h-index
2 publications
180 citations

Southeast University
Nanjing, China
2 h-index
2 publications
223 citations
1–8 of 67
3 publications · 168 citations
3
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2 publications · 30 citations
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2 publications · 60 citations
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Repository · 2 publications · 41 citations
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1 publications · 10 citations
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