Reinforcement Learning Transfer
Reinforcement Learning Transfer is a transfer learning methodology that leverages knowledge acquired in one or more source reinforcement learning tasks to improve learning efficiency, performance, or generalization in a distinct but related target reinforcement learning task.
561
Publications
34.7K
Citations
2K
Authors
546
Institutions
Publications per year
2017–2026
426
Parents
561
Learning dexterous in-hand manipulation
OpenAI Marcin Andrychowicz, Bowen Baker, Maciek Chociej et al. · The International Journal of Robotics Research · 2019 · 1.6K citations · Full text
Reinforcement Learning with Hierarchies of Machines
Ronald Parr, Stuart Russell · 1997 · 616 citations
1–5 of 561
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546
The University of Texas at Austin
Austin, United States
26 h-index
80 publications
5.7K citations
Google (United States)
Mountain View, United States
14 h-index
72 publications
4.6K citations
University of California, Berkeley
Berkeley, United States
13 h-index
37 publications
4.1K citations
Carnegie Mellon University
Pittsburgh, United States
12 h-index
23 publications
1.6K citations
Massachusetts Institute of Technology
Cambridge, United States
11 h-index
30 publications
3K citations
Tsinghua University
Beijing, China
10 h-index
28 publications
1.3K citations

Zhejiang University
Hangzhou, China
10 h-index
28 publications
1.6K citations
Nanyang Technological University
Singapore, Singapore
10 h-index
18 publications
814 citations
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Repository · 103 publications · 5K citations
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Conference · 12 publications · 751 citations
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10 publications · 333 citations
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7 publications · 270 citations
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IJRR · 5 publications · 2.2K citations
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