Difference between revisions of "Edward Lockhart"

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==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]
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* [[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]
  
 
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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]

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2020 ...

External Links

References

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