Difference between revisions of "Edward Lockhart"

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==2018 ...==
 
==2018 ...==
 
* [[Vinícius Flores Zambaldi]], [[David Raposo]], [[Adam Santoro]], [[Victor Bapst]], [[Yujia Li]], [[Igor Babuschkin]], [[Karl Tuyls]], [[David P. Reichert]], [[Timothy Lillicrap]], [[Edward Lockhart]], [[Murray Shanahan]], [[Victoria Langston]], [[Razvan Pascanu]], [[Matthew Botvinick]], [[Oriol Vinyals]], [[Peter W. Battaglia]] ('''2018'''). ''Relational Deep Reinforcement Learning''. [https://arxiv.org/abs/1806.01830 arXiv:1806.01830]
 
* [[Vinícius Flores Zambaldi]], [[David Raposo]], [[Adam Santoro]], [[Victor Bapst]], [[Yujia Li]], [[Igor Babuschkin]], [[Karl Tuyls]], [[David P. Reichert]], [[Timothy Lillicrap]], [[Edward Lockhart]], [[Murray Shanahan]], [[Victoria Langston]], [[Razvan Pascanu]], [[Matthew Botvinick]], [[Oriol Vinyals]], [[Peter W. Battaglia]] ('''2018'''). ''Relational Deep Reinforcement Learning''. [https://arxiv.org/abs/1806.01830 arXiv:1806.01830]
* [[Marc Lanctot]], [[Edward Lockhart]], [[Jean-Baptiste Lespiau]], [[Vinicius Zambaldi]], [[Satyaki Upadhyay]], [[Julien Pérolat]], [[Sriram Srinivasan]], [[Finbarr Timbers]], [[Karl Tuyls]], [[Shayegan Omidshafiei]], [[Daniel Hennes]], [[Dustin Morrill]], [[Paul Muller]], [[Timo Ewalds]], [[Ryan Faulkner]], [[János Kramár]], [[Bart De Vylder]], [[Brennan Saeta]], [[James Bradbury]], [[David Ding]], [[Sebastian Borgeaud]], [[Matthew Lai]], [[Julian Schrittwieser]], [[Thomas Anthony]], [[Edward Hughes]], [[Ivo Danihelka]], [[Jonah Ryan-Davis]] ('''2019'''). ''OpenSpiel: A Framework for Reinforcement Learning in Games''. [https://arxiv.org/abs/1908.09453 arXiv:1908.09453] <ref>[https://github.com/deepmind/open_spiel/blob/master/docs/contributing.md open_spiel/contributing.md at master · deepmind/open_spiel · GitHub]</ref>
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* [[Marc Lanctot]], [[Edward Lockhart]], [[Jean-Baptiste Lespiau]], [[Vinícius Flores Zambaldi]], [[Satyaki Upadhyay]], [[Julien Pérolat]], [[Sriram Srinivasan]], [[Finbarr Timbers]], [[Karl Tuyls]], [[Shayegan Omidshafiei]], [[Daniel Hennes]], [[Dustin Morrill]], [[Paul Muller]], [[Timo Ewalds]], [[Ryan Faulkner]], [[János Kramár]], [[Bart De Vylder]], [[Brennan Saeta]], [[James Bradbury]], [[David Ding]], [[Sebastian Borgeaud]], [[Matthew Lai]], [[Julian Schrittwieser]], [[Thomas Anthony]], [[Edward Hughes]], [[Ivo Danihelka]], [[Jonah Ryan-Davis]] ('''2019'''). ''OpenSpiel: A Framework for Reinforcement Learning in Games''. [https://arxiv.org/abs/1908.09453 arXiv:1908.09453] <ref>[https://github.com/deepmind/open_spiel/blob/master/docs/contributing.md open_spiel/contributing.md at master · deepmind/open_spiel · GitHub]</ref>
 
* [[Julian Schrittwieser]], [[Ioannis Antonoglou]], [[Thomas Hubert]], [[Karen Simonyan]], [[Laurent Sifre]], [[Simon Schmitt]], [[Arthur Guez]], [[Edward Lockhart]], [[Demis Hassabis]], [[Thore Graepel]], [[Timothy Lillicrap]], [[David Silver]] ('''2019'''). ''Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model''. [https://arxiv.org/abs/1911.08265 arXiv:1911.08265]
 
* [[Julian Schrittwieser]], [[Ioannis Antonoglou]], [[Thomas Hubert]], [[Karen Simonyan]], [[Laurent Sifre]], [[Simon Schmitt]], [[Arthur Guez]], [[Edward Lockhart]], [[Demis Hassabis]], [[Thore Graepel]], [[Timothy Lillicrap]], [[David Silver]] ('''2019'''). ''Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model''. [https://arxiv.org/abs/1911.08265 arXiv:1911.08265]
 
* [[Edward Lockhart]], [[Marc Lanctot]], [[Julien Pérolat]], [[Jean-Baptiste Lespiau]], [[Dustin Morrill]], [[Finbarr Timbers]], [[Karl Tuyls]] ('''2019'''). ''Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent''. [https://arxiv.org/abs/1903.05614 arXiv:1903.05614]
 
* [[Edward Lockhart]], [[Marc Lanctot]], [[Julien Pérolat]], [[Jean-Baptiste Lespiau]], [[Dustin Morrill]], [[Finbarr Timbers]], [[Karl Tuyls]] ('''2019'''). ''Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent''. [https://arxiv.org/abs/1903.05614 arXiv:1903.05614]
 
==2020 ...==
 
==2020 ...==
 
* [[Julian Schrittwieser]], [[Ioannis Antonoglou]], [[Thomas Hubert]], [[Karen Simonyan]], [[Laurent Sifre]], [[Simon Schmitt]], [[Arthur Guez]], [[Edward Lockhart]], [[Demis Hassabis]], [[Thore Graepel]], [[Timothy Lillicrap]], [[David Silver]] ('''2020'''). ''[https://www.nature.com/articles/s41586-020-03051-4 Mastering Atari, Go, chess and shogi by planning with a learned model]''. [https://en.wikipedia.org/wiki/Nature_%28journal%29 Nature], Vol. 588
 
* [[Julian Schrittwieser]], [[Ioannis Antonoglou]], [[Thomas Hubert]], [[Karen Simonyan]], [[Laurent Sifre]], [[Simon Schmitt]], [[Arthur Guez]], [[Edward Lockhart]], [[Demis Hassabis]], [[Thore Graepel]], [[Timothy Lillicrap]], [[David Silver]] ('''2020'''). ''[https://www.nature.com/articles/s41586-020-03051-4 Mastering Atari, Go, chess and shogi by planning with a learned model]''. [https://en.wikipedia.org/wiki/Nature_%28journal%29 Nature], Vol. 588
* [[Finbarr Timbers]], [[Edward Lockhart]], [[Martin Schmid]], [[Marc Lanctot]], [[Michael Bowling]] ('''2020'''). ''Approximate exploitability: Learning a best response in large games''. [https://arxiv.org/abs/2004.09677 arXiv:2004.09677]
+
* [[Finbarr Timbers]], [[Edward Lockhart]], [[Mathematician#MSchmid|Martin Schmid]], [[Marc Lanctot]], [[Michael Bowling]] ('''2020'''). ''Approximate exploitability: Learning a best response in large games''. [https://arxiv.org/abs/2004.09677 arXiv:2004.09677]
* [[Samuel Sokota]], [[Edward Lockhart]], [[Finbarr Timbers]], [[Elnaz Davoodi]], [[Ryan D'Orazio]], [[Neil Burch]], [[Martin Schmid]], [[Michael Bowling]], [[Marc Lanctot]] ('''2021'''). ''Solving Common-Payoff Games with Approximate Policy Iteration''. [https://arxiv.org/abs/2101.04237 arXiv:2101.04237]
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* [[Samuel Sokota]], [[Edward Lockhart]], [[Finbarr Timbers]], [[Elnaz Davoodi]], [[Ryan D'Orazio]], [[Neil Burch]], [[Mathematician#MSchmid|Martin Schmid]], [[Michael Bowling]], [[Marc Lanctot]] ('''2021'''). ''Solving Common-Payoff Games with Approximate Policy Iteration''. [https://arxiv.org/abs/2101.04237 arXiv:2101.04237]
  
 
=External Links=  
 
=External Links=  

Latest revision as of 15:19, 30 May 2021

Home * People * Edward Lockhart

Edward Lockhart [1]

Edward Lockhart,
a British computer scientist and reaearch engineer at DeepMind and head of its AI components. He holds a MA in mathematics from University of Cambridge in 1996 [2]. His current research focus is on sampling algorithms for equilibrium computation and decision-making. Edward Lockhart contributed to various reinforcement learning projects, such as OpenSpiel and MuZero [3].

Selected Publications

[4]

2018 ...

2020 ...

External Links

References

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