Difference between revisions of "Timothy Lillicrap"
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* [[Shixiang Gu]], [[Timothy Lillicrap]], [[Ilya Sutskever]], [[Sergey Levine]] ('''2016'''). ''Continuous Deep Q-Learning with Model-based Acceleration''. [https://arxiv.org/abs/1603.00748 arXiv:1603.00748] <ref>[https://en.wikipedia.org/wiki/Q-learning Q-learning from Wikipedia]</ref> | * [[Shixiang Gu]], [[Timothy Lillicrap]], [[Ilya Sutskever]], [[Sergey Levine]] ('''2016'''). ''Continuous Deep Q-Learning with Model-based Acceleration''. [https://arxiv.org/abs/1603.00748 arXiv:1603.00748] <ref>[https://en.wikipedia.org/wiki/Q-learning Q-learning from Wikipedia]</ref> | ||
* [[Shixiang Gu]], [[Ethan Holly]], [[Timothy Lillicrap]], [[Sergey Levine]] ('''2016'''). ''Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates''. [https://arxiv.org/abs/1610.00633 arXiv:1610.00633] | * [[Shixiang Gu]], [[Ethan Holly]], [[Timothy Lillicrap]], [[Sergey Levine]] ('''2016'''). ''Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates''. [https://arxiv.org/abs/1610.00633 arXiv:1610.00633] | ||
| − | * [[Shixiang Gu]], [[Timothy Lillicrap]], [[Zoubin Ghahramani]], [[Richard E. Turner]], [[Sergey Levine]] ('''2016'''). ''Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic''. [https://arxiv.org/abs/1611.02247 arXiv:1611.02247] | + | * [[Shixiang Gu]], [[Timothy Lillicrap]], [[Mathematician#ZGhahramani|Zoubin Ghahramani]], [[Richard E. Turner]], [[Sergey Levine]] ('''2016'''). ''Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic''. [https://arxiv.org/abs/1611.02247 arXiv:1611.02247] |
'''2017''' | '''2017''' | ||
* [[Yutian Chen]], [[Matthew W. Hoffman]], [[Sergio Gomez Colmenarejo]], [[Misha Denil]], [[Timothy Lillicrap]], [[Matthew Botvinick]], [[Nando de Freitas]] ('''2017'''). ''Learning to Learn without Gradient Descent by Gradient Descent''. [https://arxiv.org/abs/1611.03824v6 arXiv:1611.03824v6], [http://dblp.uni-trier.de/db/conf/icml/icml2017.html ICML 2017] | * [[Yutian Chen]], [[Matthew W. Hoffman]], [[Sergio Gomez Colmenarejo]], [[Misha Denil]], [[Timothy Lillicrap]], [[Matthew Botvinick]], [[Nando de Freitas]] ('''2017'''). ''Learning to Learn without Gradient Descent by Gradient Descent''. [https://arxiv.org/abs/1611.03824v6 arXiv:1611.03824v6], [http://dblp.uni-trier.de/db/conf/icml/icml2017.html ICML 2017] | ||
Revision as of 20:58, 14 November 2019
Home * People * Timothy Lillicrap
Timothy Lillicrap [1]
Timothy P. (Tim) Lillicrap,
a Canadian neuroscientist an AI researcher, adjunct professor at University College London, and staff research scientist at Google, DeepMind, where he is involved in the AlphaGo and AlphaZero projects mastering the games of Go, chess and Shogi. He holds a B.Sc. in cognitive science and artificial intelligence from University of Toronto in 2005, and a Ph.D. in systems neuroscience from Queen's University in 2014 under Stephen H. Scott [2] [3]. His research focuses on machine learning and statistics for optimal control and decision making, as well as using these mathematical frameworks to understand how the brain learns. He has developed algorithms and approaches for exploiting deep neural networks in the context of reinforcement learning, and new recurrent memory architectures for one-shot learning [4].
Selected Publications
2014
- Timothy Lillicrap (2014). Modelling Motor Cortex using Neural Network Controls Laws. Ph.D. Systems Neuroscience Thesis, Centre for Neuroscience Studies, Queen's University, advisor: Stephen H. Scott
2015 ...
- Timothy Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra (2015). Continuous Control with Deep Reinforcement Learning. arXiv:1509.02971
- Nicolas Heess, Jonathan J. Hunt, Timothy Lillicrap, David Silver (2015). Memory-based control with recurrent neural networks. arXiv:1512.04455
2016
- David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, Demis Hassabis (2016). Mastering the game of Go with deep neural networks and tree search. Nature, Vol. 529 » AlphaGo
- Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu (2016). Asynchronous Methods for Deep Reinforcement Learning. arXiv:1602.01783v2
- Shixiang Gu, Timothy Lillicrap, Ilya Sutskever, Sergey Levine (2016). Continuous Deep Q-Learning with Model-based Acceleration. arXiv:1603.00748 [6]
- Shixiang Gu, Ethan Holly, Timothy Lillicrap, Sergey Levine (2016). Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates. arXiv:1610.00633
- Shixiang Gu, Timothy Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine (2016). Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic. arXiv:1611.02247
2017
- Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy Lillicrap, Matthew Botvinick, Nando de Freitas (2017). Learning to Learn without Gradient Descent by Gradient Descent. arXiv:1611.03824v6, ICML 2017
- David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, Demis Hassabis (2017). Mastering the game of Go without human knowledge. Nature, Vol. 550 [7]
- David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, Demis Hassabis (2017). Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm. arXiv:1712.01815 » AlphaZero
2018
- Jack W. Rae, Chris Dyer, Peter Dayan, Timothy Lillicrap (2018). Fast Parametric Learning with Activation Memorization. arXiv:1803.10049
- David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, Demis Hassabis (2018). A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science, Vol. 362, No. 6419 [8]
External Links
- homepage of timothy lillicrap
- Timothy P. Lillicrap - Google Scholar Citations
- Tim Lillicrap - Data efficient Deep Reinforcement Learning for Continuous Control, YouTube Video
References
- ↑ Image captured from the Data efficient Deep Reinforcement Learning for Continuous Control - Video at 20:21
- ↑ Timothy Lillicrap (2014). Modelling Motor Cortex using Neural Network Controls Laws. Ph.D. Systems Neuroscience Thesis, Centre for Neuroscience Studies, Queen's University, advisor: Stephen H. Scott
- ↑ Curriculum Vitae - Timothy P. Lillicrap (pdf)
- ↑ timothy lillicrap - research
- ↑ dblp: Timothy P. Lillicrap
- ↑ Q-learning from Wikipedia
- ↑ AlphaGo Zero: Learning from scratch by Demis Hassabis and David Silver, DeepMind, October 18, 2017
- ↑ AlphaZero: Shedding new light on the grand games of chess, shogi and Go by David Silver, Thomas Hubert, Julian Schrittwieser and Demis Hassabis, DeepMind, December 03, 2018
