Edward Lockhart
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Edward Lockhart[1]
Edward Lockhart,
a British computer scientist and reaearch engineer atDeepMind and head of its AI components. He holds a MA in mathematics fromUniversity of Cambridge in 1996[2]. His current research focus is onsampling algorithms forequilibrium computation anddecision-making.Edward Lockhart contributed to variousreinforcement learning projects, such asOpenSpiel andMuZero[3].
Selected Publications
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.arXiv:1806.01830
- 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.arXiv:1908.09453[5]
- 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.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.arXiv:1903.05614
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).Mastering Atari, Go, chess and shogi by planning with a learned model.Nature, Vol. 588
- Finbarr Timbers,Edward Lockhart,Martin Schmid,Marc Lanctot,Michael Bowling (2020).Approximate exploitability: Learning a best response in large games.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.arXiv:2101.04237
