Deterministic Policy Learning
Deterministic Policy Learning is a method in reinforcement learning whereby the policy, as a mapping from states to actions, is learned to output a single, specific action for each state without stochastic variation.
301
Publications
30.5K
Citations
1K
Authors
326
Institutions
Publications per year
2017–2026
265
Parents
301
Continuous control with deep reinforcement learning
Timothy Lillicrap, Jonathan J. Hunt, Alexander Pritzel et al. · arXiv (Cornell University) · 2016 · 6.8K citations · Full text
Computing, Computer Science, Physics +17
Continuous control with deep reinforcement learning
Timothy Lillicrap, Jonathan J. Hunt, Alexander Pritzel et al. · arXiv (Cornell University) · 2015 · 5.4K citations · Full text
Computing, Computer Science, Physics +17
Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess et al. · HAL (Le Centre pour la Communication Scientifique Directe) · 2014 · 1.7K citations · Full text
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326

South China University of Technology
Guangzhou, China
16 h-index
32 publications
1.8K citations
Tsinghua University
Beijing, China
13 h-index
45 publications
3.5K citations
Longyan University
Longyan, China
10 h-index
21 publications
1.1K citations

Shanghai Jiao Tong University
Shanghai, China
7 h-index
29 publications
641 citations

Beijing Institute of Technology
Beijing, China
6 h-index
22 publications
1.3K citations

Chinese Academy of Sciences
Beijing, China
6 h-index
14 publications
343 citations

University of Hong Kong
Pok Fu Lam, Hong Kong
6 h-index
8 publications
252 citations

Northwestern Polytechnical University
Xi'an, China
5 h-index
17 publications
971 citations
1–8 of 326
Repository · 25 publications · 15.9K citations
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12 publications · 583 citations
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8 publications · 994 citations
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7 publications · 214 citations
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7 publications · 199 citations
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