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Game Plan: What AI can do for Football, and What Football can do for AI

Nov 18, 2020
Karl Tuyls, Shayegan Omidshafiei, Paul Muller, Zhe Wang, Jerome Connor, Daniel Hennes, Ian Graham, William Spearman, Tim Waskett, Dafydd Steele, Pauline Luc, Adria Recasens, Alexandre Galashov, Gregory Thornton, Romuald Elie, Pablo Sprechmann, Pol Moreno, Kris Cao, Marta Garnelo, Praneet Dutta, Michal Valko, Nicolas Heess, Alex Bridgland, Julien Perolat, Bart De Vylder, Ali Eslami, Mark Rowland, Andrew Jaegle, Remi Munos, Trevor Back, Razia Ahamed, Simon Bouton, Nathalie Beauguerlange, Jackson Broshear, Thore Graepel, Demis Hassabis


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Revisiting Fundamentals of Experience Replay

Jul 13, 2020
William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney

* Published at ICML 2020. First two authors contributed equally and code available at https://github.com/google-research/google-research/tree/master/experience_replay 

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The Value-Improvement Path: Towards Better Representations for Reinforcement Learning

Jun 03, 2020
Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G. Bellemare, David Silver


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Navigating the Landscape of Games

May 04, 2020
Shayegan Omidshafiei, Karl Tuyls, Wojciech M. Czarnecki, Francisco C. Santos, Mark Rowland, Jerome Connor, Daniel Hennes, Paul Muller, Julien Perolat, Bart De Vylder, Audrunas Gruslys, Remi Munos


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From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization

Feb 19, 2020
Julien Perolat, Remi Munos, Jean-Baptiste Lespiau, Shayegan Omidshafiei, Mark Rowland, Pedro Ortega, Neil Burch, Thomas Anthony, David Balduzzi, Bart De Vylder, Georgios Piliouras, Marc Lanctot, Karl Tuyls

* 43 pages 

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Multiagent Evaluation under Incomplete Information

Oct 30, 2019
Mark Rowland, Shayegan Omidshafiei, Karl Tuyls, Julien Perolat, Michal Valko, Georgios Piliouras, Remi Munos


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Conditional Importance Sampling for Off-Policy Learning

Oct 16, 2019
Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, RĂ©mi Munos, Will Dabney


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Adaptive Trade-Offs in Off-Policy Learning

Oct 16, 2019
Mark Rowland, Will Dabney, RĂ©mi Munos


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A Generalized Training Approach for Multiagent Learning

Sep 27, 2019
Paul Muller, Shayegan Omidshafiei, Mark Rowland, Karl Tuyls, Julien Perolat, Siqi Liu, Daniel Hennes, Luke Marris, Marc Lanctot, Edward Hughes, Zhe Wang, Guy Lever, Nicolas Heess, Thore Graepel, Remi Munos


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Meta-learning of Sequential Strategies

May 08, 2019
Pedro A. Ortega, Jane X. Wang, Mark Rowland, Tim Genewein, Zeb Kurth-Nelson, Razvan Pascanu, Nicolas Heess, Joel Veness, Alex Pritzel, Pablo Sprechmann, Siddhant M. Jayakumar, Tom McGrath, Kevin Miller, Mohammad Azar, Ian Osband, Neil Rabinowitz, András György, Silvia Chiappa, Simon Osindero, Yee Whye Teh, Hado van Hasselt, Nando de Freitas, Matthew Botvinick, Shane Legg

* DeepMind Technical Report (15 pages, 6 figures) 

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Orthogonal Estimation of Wasserstein Distances

Apr 05, 2019
Mark Rowland, Jiri Hron, Yunhao Tang, Krzysztof Choromanski, Tamas Sarlos, Adrian Weller

* Published at AISTATS 2019 

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Statistics and Samples in Distributional Reinforcement Learning

Feb 21, 2019
Mark Rowland, Robert Dadashi, Saurabh Kumar, RĂ©mi Munos, Marc G. Bellemare, Will Dabney


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The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings

Sep 03, 2018
Krzysztof Choromanski, Mark Rowland, Adrian Weller


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Gaussian Process Behaviour in Wide Deep Neural Networks

Aug 16, 2018
Alexander G. de G. Matthews, Mark Rowland, Jiri Hron, Richard E. Turner, Zoubin Ghahramani

* This work substantially extends the work of Matthews et al. (2018) published at the International Conference on Learning Representations (ICLR) 2018 

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Antithetic and Monte Carlo kernel estimators for partial rankings

Jul 25, 2018
Maria Lomeli, Mark Rowland, Arthur Gretton, Zoubin Ghahramani


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Structured Evolution with Compact Architectures for Scalable Policy Optimization

Jun 12, 2018
Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E. Turner, Adrian Weller


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An Analysis of Categorical Distributional Reinforcement Learning

Feb 22, 2018
Mark Rowland, Marc G. Bellemare, Will Dabney, RĂ©mi Munos, Yee Whye Teh


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Distributional Reinforcement Learning with Quantile Regression

Oct 27, 2017
Will Dabney, Mark Rowland, Marc G. Bellemare, RĂ©mi Munos


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Magnetic Hamiltonian Monte Carlo

Aug 19, 2017
Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, Richard Turner

* 34th International Conference on Machine Learning (ICML 2017) 

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Black-box $α$-divergence Minimization

Jun 01, 2016
José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland, Daniel Hernández-Lobato, Thang Bui, Richard E. Turner

* Accepted at ICML 2016. The first version (v1) was presented at NIPS workshops on Advances in Approximate Bayesian Inference and Black Box Learning and Inference 

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