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Home * People * Edward Lockhart
Edward Lockhart [1]
Edward Lockhart,
a British computer scientist at DeepMind and head of 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
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, 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. 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
