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'''[[Main Page|Home]] * [[People]] * Timothy Lillicrap'''
[[FILE:TimothyLillicrap.jpg|border|right|thumb| Timothy Lillicrap <ref>Image captured from the [[Timothy Lillicrap#DRLVideo|Data efficient Deep Reinforcement Learning for Continuous Control - Video]] at 20:21</ref> ]]
'''Timothy P. (Tim) Lillicrap''',<br/>
a Canadian [https://en.wikipedia.org/wiki/Neuroscientist neuroscientist] an [[Artificial Intelligence|AI]] researcher, adjunct professor at [https://en.wikipedia.org/wiki/University_College_London 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|chess]] and [[Shogi]]. He holds a B.Sc. in [[Cognition |cognitive science]] and [[Artificial Intelligence|artificial intelligence]] from [[University of Toronto]] in 2005, and a Ph.D. in [https://en.wikipedia.org/wiki/Systems_neuroscience systems neuroscience] from [https://en.wikipedia.org/wiki/Queen%27s_University Queen's University] in 2014 under [https://en.wikipedia.org/wiki/Stephen_H._Scott Stephen H. Scott] <ref>[[Timothy Lillicrap]] ('''2014'''). ''Modelling Motor Cortex using Neural Network Controls Laws''. Ph.D. Systems Neuroscience Thesis, Centre for Neuroscience Studies, [https://en.wikipedia.org/wiki/Queen%27s_University Queen's University], advisor: [https://en.wikipedia.org/wiki/Stephen_H._Scott Stephen H. Scott]</ref> <ref>[http://contrastiveconvergence.net/~timothylillicrap/files/resume/timothy_lillicrap_cv.pdf Curriculum Vitae - Timothy P. Lillicrap] (pdf)</ref>. His research focuses on [[Learning|machine learning]] and [https://en.wikipedia.org/wiki/Statistics statistics] for [https://en.wikipedia.org/wiki/Optimal_control optimal control] and [https://en.wikipedia.org/wiki/Decision-making decision making], as well as using these mathematical frameworks to understand how the [https://en.wikipedia.org/wiki/Brain brain] learns. He has developed algorithms and approaches for exploiting [[Neural Networks#Deep|deep neural networks]] in the context of [[Reinforcement Learning|reinforcement learning]], and new [https://en.wikipedia.org/wiki/Recurrent_neural_network recurrent memory architectures] for [https://en.wikipedia.org/wiki/One-shot_learning one-shot learning] <ref>[http://contrastiveconvergence.net/~timothylillicrap/research.php timothy lillicrap - research]</ref>.
=Selected Publications=
<ref>[http://dblp.uni-trier.de/pers/hd/l/Lillicrap:Timothy_P= dblp: Timothy P. Lillicrap]</ref>
==2014==
* [[Timothy Lillicrap]] ('''2014'''). ''Modelling Motor Cortex using Neural Network Controls Laws''. Ph.D. Systems Neuroscience Thesis, Centre for Neuroscience Studies, [https://en.wikipedia.org/wiki/Queen%27s_University Queen's University], advisor: [https://en.wikipedia.org/wiki/Stephen_H._Scott 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''. [https://arxiv.org/abs/1509.02971 arXiv:1509.02971]
* [[Nicolas Heess]], [[Jonathan J. Hunt]], [[Timothy Lillicrap]], [[David Silver]] ('''2015'''). ''Memory-based control with recurrent neural networks''. [https://arxiv.org/abs/1512.04455 arXiv:1512.04455]
'''2016'''
* [[David Silver]], [[Shih-Chieh Huang|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'''). ''[http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html Mastering the game of Go with deep neural networks and tree search]''. [https://en.wikipedia.org/wiki/Nature_%28journal%29 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''. [https://arxiv.org/abs/1602.01783 arXiv:1602.01783v2]
* [[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]], [[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]
'''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]
* [[David Silver]], [[Julian Schrittwieser]], [[Karen Simonyan]], [[Ioannis Antonoglou]], [[Shih-Chieh Huang|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'''). ''[https://www.nature.com/nature/journal/v550/n7676/full/nature24270.html Mastering the game of Go without human knowledge]''. [https://en.wikipedia.org/wiki/Nature_%28journal%29 Nature], Vol. 550 <ref>[https://deepmind.com/blog/alphago-zero-learning-scratch/ AlphaGo Zero: Learning from scratch] by [[Demis Hassabis]] and [[David Silver]], [[DeepMind]], October 18, 2017</ref>
* [[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''. [https://arxiv.org/abs/1712.01815 arXiv:1712.01815] » [[AlphaZero]]
=External Links=
* [http://contrastiveconvergence.net/~timothylillicrap/index.php homepage of timothy lillicrap]
* [https://scholar.google.co.uk/citations?user=htPVdRMAAAAJ&hl=en Timothy P. Lillicrap - Google Scholar Citations]
* <span id="DRLVideo"></span>[https://youtu.be/M6nfipCxQBc Tim Lillicrap - Data efficient Deep Reinforcement Learning for Continuous Control], [https://en.wikipedia.org/wiki/YouTube YouTube] Video
: {{#evu:https://www.youtube.com/watch?v=M6nfipCxQBc|alignment=left|valignment=top}}
=References=
<references />
'''[[People|Up one level]]'''
[[FILE:TimothyLillicrap.jpg|border|right|thumb| Timothy Lillicrap <ref>Image captured from the [[Timothy Lillicrap#DRLVideo|Data efficient Deep Reinforcement Learning for Continuous Control - Video]] at 20:21</ref> ]]
'''Timothy P. (Tim) Lillicrap''',<br/>
a Canadian [https://en.wikipedia.org/wiki/Neuroscientist neuroscientist] an [[Artificial Intelligence|AI]] researcher, adjunct professor at [https://en.wikipedia.org/wiki/University_College_London 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|chess]] and [[Shogi]]. He holds a B.Sc. in [[Cognition |cognitive science]] and [[Artificial Intelligence|artificial intelligence]] from [[University of Toronto]] in 2005, and a Ph.D. in [https://en.wikipedia.org/wiki/Systems_neuroscience systems neuroscience] from [https://en.wikipedia.org/wiki/Queen%27s_University Queen's University] in 2014 under [https://en.wikipedia.org/wiki/Stephen_H._Scott Stephen H. Scott] <ref>[[Timothy Lillicrap]] ('''2014'''). ''Modelling Motor Cortex using Neural Network Controls Laws''. Ph.D. Systems Neuroscience Thesis, Centre for Neuroscience Studies, [https://en.wikipedia.org/wiki/Queen%27s_University Queen's University], advisor: [https://en.wikipedia.org/wiki/Stephen_H._Scott Stephen H. Scott]</ref> <ref>[http://contrastiveconvergence.net/~timothylillicrap/files/resume/timothy_lillicrap_cv.pdf Curriculum Vitae - Timothy P. Lillicrap] (pdf)</ref>. His research focuses on [[Learning|machine learning]] and [https://en.wikipedia.org/wiki/Statistics statistics] for [https://en.wikipedia.org/wiki/Optimal_control optimal control] and [https://en.wikipedia.org/wiki/Decision-making decision making], as well as using these mathematical frameworks to understand how the [https://en.wikipedia.org/wiki/Brain brain] learns. He has developed algorithms and approaches for exploiting [[Neural Networks#Deep|deep neural networks]] in the context of [[Reinforcement Learning|reinforcement learning]], and new [https://en.wikipedia.org/wiki/Recurrent_neural_network recurrent memory architectures] for [https://en.wikipedia.org/wiki/One-shot_learning one-shot learning] <ref>[http://contrastiveconvergence.net/~timothylillicrap/research.php timothy lillicrap - research]</ref>.
=Selected Publications=
<ref>[http://dblp.uni-trier.de/pers/hd/l/Lillicrap:Timothy_P= dblp: Timothy P. Lillicrap]</ref>
==2014==
* [[Timothy Lillicrap]] ('''2014'''). ''Modelling Motor Cortex using Neural Network Controls Laws''. Ph.D. Systems Neuroscience Thesis, Centre for Neuroscience Studies, [https://en.wikipedia.org/wiki/Queen%27s_University Queen's University], advisor: [https://en.wikipedia.org/wiki/Stephen_H._Scott 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''. [https://arxiv.org/abs/1509.02971 arXiv:1509.02971]
* [[Nicolas Heess]], [[Jonathan J. Hunt]], [[Timothy Lillicrap]], [[David Silver]] ('''2015'''). ''Memory-based control with recurrent neural networks''. [https://arxiv.org/abs/1512.04455 arXiv:1512.04455]
'''2016'''
* [[David Silver]], [[Shih-Chieh Huang|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'''). ''[http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html Mastering the game of Go with deep neural networks and tree search]''. [https://en.wikipedia.org/wiki/Nature_%28journal%29 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''. [https://arxiv.org/abs/1602.01783 arXiv:1602.01783v2]
* [[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]], [[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]
'''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]
* [[David Silver]], [[Julian Schrittwieser]], [[Karen Simonyan]], [[Ioannis Antonoglou]], [[Shih-Chieh Huang|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'''). ''[https://www.nature.com/nature/journal/v550/n7676/full/nature24270.html Mastering the game of Go without human knowledge]''. [https://en.wikipedia.org/wiki/Nature_%28journal%29 Nature], Vol. 550 <ref>[https://deepmind.com/blog/alphago-zero-learning-scratch/ AlphaGo Zero: Learning from scratch] by [[Demis Hassabis]] and [[David Silver]], [[DeepMind]], October 18, 2017</ref>
* [[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''. [https://arxiv.org/abs/1712.01815 arXiv:1712.01815] » [[AlphaZero]]
=External Links=
* [http://contrastiveconvergence.net/~timothylillicrap/index.php homepage of timothy lillicrap]
* [https://scholar.google.co.uk/citations?user=htPVdRMAAAAJ&hl=en Timothy P. Lillicrap - Google Scholar Citations]
* <span id="DRLVideo"></span>[https://youtu.be/M6nfipCxQBc Tim Lillicrap - Data efficient Deep Reinforcement Learning for Continuous Control], [https://en.wikipedia.org/wiki/YouTube YouTube] Video
: {{#evu:https://www.youtube.com/watch?v=M6nfipCxQBc|alignment=left|valignment=top}}
=References=
<references />
'''[[People|Up one level]]'''
