Mohammad Gheshlaghi Azar - AI Profile

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DeepMind

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I am a research scientist at DeepMind AI woking on broad range of topics from Deep RL to self-supervised learning. My main passion is to gain better understanding of the concept of intelligence by looking deeper on its governing mathematical principles.

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The Advantage Regret-Matching Actor-Critic

Audrūnas Gruslys,Marc Lanctot,Rémi Munos, Finbarr Timbers, Martin Schmid,Julien Perolat, Dustin Morrill, Vinicius Zambaldi, Jean-Baptiste Lespiau, John Schultz,Mohammad Gheshlaghi Azar, Michael Bowling

Bibtex

Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning

Grill Jean-Bastien,Strub Florian, Altché Florent,Tallec Corentin,Richemond Pierre H.,Buchatskaya Elena,Doersch Carl,Pires Bernardo Avila,Guo Zhaohan Daniel,Azar Mohammad Gheshlaghi,Piot Bilal,Kavukcuoglu Koray

NIPS 2020, (2020)

Cited by499EIBibtex

Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning

Zhaohan Guo,Bernardo Avila Pires,Mohammad Gheshlaghi Azar,Bilal Piot, Florent Altché, Jean-Bastien Grill,Remi Munos

international conference on machine learning, (2020)

Cited by12Bibtex

Fast computation of Nash Equilibria in Imperfect Information Games

Remi Munos,Julien Perolat, Jean-Baptiste Lespiau,Mark Rowland,Bart De Vylder,Marc Lanctot, Finbarr Timbers,Daniel Hennes,Shayegan Omidshafiei, Audrunas Gruslys,Mohammad Gheshlaghi Azar,Edward Lockhart

ICML, pp.7119-7129, (2020)

EIBibtex

Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning

Guo Daniel,Pires Bernardo Avila,Piot Bilal,Grill Jean-bastien, Altché Florent,Munos Rémi,Azar Mohammad Gheshlaghi

ICML, pp.3875-3886, (2020)

Cited by28EIBibtex

Neural Predictive Belief Representations

Guo Zhaohan Daniel,Azar Mohammad Gheshlaghi,Piot Bilal,Pires Bernardo A.,Munos Rémi

Cited by25Bibtex

World Discovery Models

Azar Mohammad Gheshlaghi,Piot Bilal,Pires Bernardo Avila, Grill Jean-Bastian, Altché Florent,Munos Rémi

Cited by13Bibtex

Hindsight Credit Assignment

Anna Harutyunyan,Will Dabney, Thomas Mesnard,Mohammad Gheshlaghi Azar,Bilal Piot,Nicolas Heess,Hado P. van Hasselt, Gregory Wayne,Satinder Singh,Doina Precup,Remi Munos

NeurIPS, pp.12467-12476, (2019)

Cited by23EIBibtex

Meta-learning of Sequential Strategies

Pedro A. Ortega,Jane X. Wang,Mark Rowland,Tim Genewein,Zeb Kurth-Nelson,Razvan Pascanu,Nicolas Heess,Joel Veness, Alexander Pritzel,Pablo Sprechmann, Siddhant M. Jayakumar, Tom McGrath

arXiv: Learning, (2019)

Cited by29EIBibtex

Noisy Networks for Exploration

Meire Fortunato,Mohammad Gheshlaghi Azar,Bilal Piot, Jacob Menick, Ian Osband,Alex Graves,Vlad Mnih,Rémi Munos,Demis Hassabis,Olivier Pietquin,Charles Blundell,Shane Legg

international conference on learning representations, (2019)

Cited by499EIBibtex

Rainbow: Combining Improvements in Deep Reinforcement Learning

Matteo Hessel,Joseph Modayil,Hado van Hasselt,Tom Schaul,Georg Ostrovski,Will Dabney, Daniel Horgan,Bilal Piot,Mohammad Gheshlaghi Azar,David Silver

national conference on artificial intelligence, (2018)

Cited by1133EIBibtex

The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning

Audrunas Gruslys,Will Dabney,Mohammad Gheshlaghi Azar,Marc G. Bellemare,Rémi Munos

international conference on learning representations, (2018)

Cited by51EIBibtex

Neural Predictive Belief Representations

Zhaohan Daniel Guo,Mohammad Gheshlaghi Azar,Bilal Piot,Bernardo A. Pires, Toby Pohlen,Rémi Munos

arXiv: Learning, (2018)

Cited by17EIBibtex

Observe and Look Further: Achieving Consistent Performance on Atari

Tobias Pohlen,Bilal Piot,Todd Hester,Mohammad Gheshlaghi Azar, Dan Horgan,David Budden, Gabriel Barth-Maron,Hado van Hasselt, John Quan, Mel Vecerík,Matteo Hessel,Rémi Munos

arXiv: Learning, (2018)

Cited by43EIBibtex

A cryptography-based approach for movement decoding

Eva L Dyer,Mohammad Gheshlaghi Azar, Matthew G Perich,Hugo L Fernandes,Stephanie Naufel,Lee E Miller,Konrad P Körding

NATURE BIOMEDICAL ENGINEERING, no. 12.0 (2017): 967.0-+

Cited by2WOSBibtex

Minimax Regret Bounds for Reinforcement Learning

Mohammad Gheshlaghi Azar, Ian Osband,Rémi Munos

ICML, (2017): 263-272

Cited by325EIBibtex

Convex Relaxation Regression: Black-Box Optimization of Smooth Functions by Learning Their Convex Envelopes

Mohammad Gheshlaghi Azar,Eva L. Dyer,Konrad P. Körding

UAI, (2016): 22-31

EIBibtex

Correcting Multivariate Auto-Regressive Models for the Influence of Unobserved Common Input

Vicenç Gómez,Mohammad Gheshlaghi Azar,Hilbert J. Kappen

Frontiers in Artificial Intelligence and Applications, (2016): 177-186

EIBibtex

Stochastic Optimization of a Locally Smooth Function under Correlated Bandit Feedback

Mohammad Gheshlaghi Azar,Alessandro Lazaric,Emma Brunskill

CoRR, (2014)

Cited by5EIBibtex

Online Stochastic Optimization under Correlated Bandit Feedback

Mohammad Gheshlaghi Azar,Alessandro Lazaric,Emma Brunskill

ICML, pp.1557-1565, (2014)

Cited by48EIBibtex

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