Apprentissage par renforcement profond (French Wikipedia)
Analysis of information sources in references of the Wikipedia article "Apprentissage par renforcement profond" in French language version.
refsWebsite
Global rank
French rank
18th place
118th place
low place
low place
low place
low place
234th place
147th place
1st place
1st place
low place
low place
1,559th place
1,879th place
low place
low place
9th place
19th place
arxiv.org
- Francois-Lavet, Henderson, Islam et Bellemare, « An Introduction to Deep Reinforcement Learning », Foundations and Trends in Machine Learning, vol. 11, nos 3–4, , p. 219–354 (ISSN 1935-8237, DOI 10.1561/2200000071, Bibcode 2018arXiv181112560F, arXiv 1811.12560).
- Schrittwieser, Antonoglou, Hubert et Simonyan, « Mastering Atari, Go, chess and shogi by planning with a learned model », Nature, vol. 588, no 7839, , p. 604–609 (PMID 33361790, DOI 10.1038/s41586-020-03051-4, Bibcode 2020Natur.588..604S, arXiv 1911.08265, lire en ligne)
- Levine, Finn, Darrell et Abbeel, « End-to-end training of deep visuomotor policies », JMLR, vol. 17, (arXiv 1504.00702, lire en ligne)
- OpenAI « Solving Rubik's Cube with a Robot Hand » () (arXiv 1910.07113, lire en ligne)
- John Schulman, Sergey Levine, Philipp Moritz, Michael Jordan et Pieter Abbeel « Trust Region Policy Optimization » () (arXiv 1502.05477, lire en ligne)
—International Conference on Machine Learning (ICML) - John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford et Oleg Klimov « Proximal Policy Optimization Algorithms » () (arXiv 1707.06347, lire en ligne)
- Timothy Lillicrap, Jonathan Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver et Daan Wierstra « Continuous control with deep reinforcement learning » () (arXiv 1509.02971, lire en ligne)
—International Conference on Learning Representations (ICLR) - Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirzi, Alex Graves, Tim Harley, Timothy Lillicrap, David Silver et Koray Kavukcuoglu « Asynchronous Methods for Deep Reinforcement Learning » () (arXiv 1602.01783, lire en ligne)
—International Conference on Machine Learning (ICML) - Tuomas Haarnoja, Aurick Zhou, Sergey Levine et Pieter Abbeel « Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor » () (arXiv 1801.01290, lire en ligne)
—International Conference on Machine Learning (ICML) - (en) Patrik Reizinger et Márton Szemenyei, « Attention-based Curiosity-driven Exploration in Deep Reinforcement Learning », IEEE, (arXiv 1910.10840)
- (en) « Maximum Entropy Deep Inverse Reinforcement Learning », sur Arxiv,
- Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin et Pieter Abbeel « Hindsight Experience Replay » () (arXiv 1707.01495, lire en ligne)
—Advances in Neural Information Processing Systems (NeurIPS) - (en) Charles Packer, Katelyn Gao, Jernej Kos, Philipp Krähenbühl, Vladlen Koltun et al., « Assessing Generalization in Deep Reinforcement Learning », .
athenasc.com
- John Bertsekas et Dimitri Tsitsiklis, Neuro-Dynamic Programming, Athena Scientific, (ISBN 1-886529-10-8, lire en ligne)
bkgm.com
- Tesauro, « Temporal Difference Learning and TD-Gammon », Communications of the ACM, vol. 38, no 3, , p. 58–68 (DOI 10.1145/203330.203343, lire en ligne [archive du ], consulté le )
deepmind.com
doi.org
dx.doi.org
- Francois-Lavet, Henderson, Islam et Bellemare, « An Introduction to Deep Reinforcement Learning », Foundations and Trends in Machine Learning, vol. 11, nos 3–4, , p. 219–354 (ISSN 1935-8237, DOI 10.1561/2200000071, Bibcode 2018arXiv181112560F, arXiv 1811.12560).
- Tesauro, « Temporal Difference Learning and TD-Gammon », Communications of the ACM, vol. 38, no 3, , p. 58–68 (DOI 10.1145/203330.203343, lire en ligne [archive du ], consulté le )
- Mnih et al., « Human-level control through deep reinforcement learning », Nature, vol. 518, no 7540, , p. 529–533 (PMID 25719670, DOI 10.1038/nature14236, Bibcode 2015Natur.518..529M)
- Silver, Huang, Maddison et Guez, « Mastering the game of Go with deep neural networks and tree search », Nature, vol. 529, no 7587, , p. 484–489 (ISSN 0028-0836, PMID 26819042, DOI 10.1038/nature16961, Bibcode 2016Natur.529..484S)
- Schrittwieser, Antonoglou, Hubert et Simonyan, « Mastering Atari, Go, chess and shogi by planning with a learned model », Nature, vol. 588, no 7839, , p. 604–609 (PMID 33361790, DOI 10.1038/s41586-020-03051-4, Bibcode 2020Natur.588..604S, arXiv 1911.08265, lire en ligne)
- Bellemare, Candido, Castro et Gong, « Autonomous navigation of stratospheric balloons using reinforcement learning », Nature, vol. 588, no 7836, , p. 77–82 (PMID 33268863, DOI 10.1038/s41586-020-2939-8, Bibcode 2020Natur.588...77B, lire en ligne)
- Williams, « Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning », Machine Learning, vol. 8, nos 3–4, , p. 229–256 (DOI 10.1007/BF00992696)
harvard.edu
ui.adsabs.harvard.edu
- Francois-Lavet, Henderson, Islam et Bellemare, « An Introduction to Deep Reinforcement Learning », Foundations and Trends in Machine Learning, vol. 11, nos 3–4, , p. 219–354 (ISSN 1935-8237, DOI 10.1561/2200000071, Bibcode 2018arXiv181112560F, arXiv 1811.12560).
- Mnih et al., « Human-level control through deep reinforcement learning », Nature, vol. 518, no 7540, , p. 529–533 (PMID 25719670, DOI 10.1038/nature14236, Bibcode 2015Natur.518..529M)
- Silver, Huang, Maddison et Guez, « Mastering the game of Go with deep neural networks and tree search », Nature, vol. 529, no 7587, , p. 484–489 (ISSN 0028-0836, PMID 26819042, DOI 10.1038/nature16961, Bibcode 2016Natur.529..484S)
- Schrittwieser, Antonoglou, Hubert et Simonyan, « Mastering Atari, Go, chess and shogi by planning with a learned model », Nature, vol. 588, no 7839, , p. 604–609 (PMID 33361790, DOI 10.1038/s41586-020-03051-4, Bibcode 2020Natur.588..604S, arXiv 1911.08265, lire en ligne)
- Bellemare, Candido, Castro et Gong, « Autonomous navigation of stratospheric balloons using reinforcement learning », Nature, vol. 588, no 7836, , p. 77–82 (PMID 33268863, DOI 10.1038/s41586-020-2939-8, Bibcode 2020Natur.588...77B, lire en ligne)
issn.org
portal.issn.org
- Francois-Lavet, Henderson, Islam et Bellemare, « An Introduction to Deep Reinforcement Learning », Foundations and Trends in Machine Learning, vol. 11, nos 3–4, , p. 219–354 (ISSN 1935-8237, DOI 10.1561/2200000071, Bibcode 2018arXiv181112560F, arXiv 1811.12560).
- Silver, Huang, Maddison et Guez, « Mastering the game of Go with deep neural networks and tree search », Nature, vol. 529, no 7587, , p. 484–489 (ISSN 0028-0836, PMID 26819042, DOI 10.1038/nature16961, Bibcode 2016Natur.529..484S)
jmlr.org
- Levine, Finn, Darrell et Abbeel, « End-to-end training of deep visuomotor policies », JMLR, vol. 17, (arXiv 1504.00702, lire en ligne)
mlr.press
proceedings.mlr.press
- Tom Schaul, Daniel Horgan, Karol Gregor et David Silver « Universal Value Function Approximators » () (lire en ligne)
—International Conference on Machine Learning (ICML)
nature.com
- Schrittwieser, Antonoglou, Hubert et Simonyan, « Mastering Atari, Go, chess and shogi by planning with a learned model », Nature, vol. 588, no 7839, , p. 604–609 (PMID 33361790, DOI 10.1038/s41586-020-03051-4, Bibcode 2020Natur.588..604S, arXiv 1911.08265, lire en ligne)
- Bellemare, Candido, Castro et Gong, « Autonomous navigation of stratospheric balloons using reinforcement learning », Nature, vol. 588, no 7836, , p. 77–82 (PMID 33268863, DOI 10.1038/s41586-020-2939-8, Bibcode 2020Natur.588...77B, lire en ligne)
nih.gov
ncbi.nlm.nih.gov
- Mnih et al., « Human-level control through deep reinforcement learning », Nature, vol. 518, no 7540, , p. 529–533 (PMID 25719670, DOI 10.1038/nature14236, Bibcode 2015Natur.518..529M)
- Silver, Huang, Maddison et Guez, « Mastering the game of Go with deep neural networks and tree search », Nature, vol. 529, no 7587, , p. 484–489 (ISSN 0028-0836, PMID 26819042, DOI 10.1038/nature16961, Bibcode 2016Natur.529..484S)
- Schrittwieser, Antonoglou, Hubert et Simonyan, « Mastering Atari, Go, chess and shogi by planning with a learned model », Nature, vol. 588, no 7839, , p. 604–609 (PMID 33361790, DOI 10.1038/s41586-020-03051-4, Bibcode 2020Natur.588..604S, arXiv 1911.08265, lire en ligne)
- Bellemare, Candido, Castro et Gong, « Autonomous navigation of stratospheric balloons using reinforcement learning », Nature, vol. 588, no 7836, , p. 77–82 (PMID 33268863, DOI 10.1038/s41586-020-2939-8, Bibcode 2020Natur.588...77B, lire en ligne)
oita-u.ac.jp
shws.cc.oita-u.ac.jp
- Katsunari Shibata et Yoichi Okabe « Reinforcement Learning When Visual Sensory Signals are Directly Given as Inputs » () (lire en ligne)
—International Conference on Neural Networks (ICNN) 1997 - Katsunari Shibata et Masaru Iida « Acquisition of Box Pushing by Direct-Vision-Based Reinforcement Learning » () (lire en ligne)
—SICE Annual Conference 2003 - Hiroki Utsunomiya et Katsunari Shibata « Contextual Behavior and Internal Representations Acquired by Reinforcement Learning with a Recurrent Neural Network in a Continuous State and Action Space Task » () (lire en ligne)
—International Conference on Neural Information Processing (ICONIP) '08 - Katsunari Shibata et Tomohiko Kawano « Learning of Action Generation from Raw Camera Images in a Real-World-like Environment by Simple Coupling of Reinforcement Learning and a Neural Network » () (lire en ligne)
—International Conference on Neural Information Processing (ICONIP) '08
openai.com
- « OpenAI - Solving Rubik's Cube With A Robot Hand », OpenAI
toronto.edu
cs.toronto.edu
- Volodymyr Mnih « Playing Atari with Deep Reinforcement Learning » () (lire en ligne)
—NIPS Deep Learning Workshop 2013. - Volodymyr Mnih « Playing Atari with Deep Reinforcement Learning » () (lire en ligne)
—NIPS Deep Learning Workshop 2013
web.archive.org
- Tesauro, « Temporal Difference Learning and TD-Gammon », Communications of the ACM, vol. 38, no 3, , p. 58–68 (DOI 10.1145/203330.203343, lire en ligne [archive du ], consulté le )
youtube.com
- George Hotz, interview par Lex Fridman, Winning - A Reinforcement Learning Approach,