Gabriel Barth-Maron

According to our database, Gabriel Barth-Maron authored at least 13 papers between 2014 and 2022.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2022

A Generalist Agent.

Scott E. Reed, Konrad Zolna, Emilio Parisotto, Sergio Gómez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, Tom Eccles, Jake Bruce, Ali Razavi, Ashley Edwards, Nicolas Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, Nando de Freitas

Trans. Mach. Learn. Res., 2022

QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement Learning.

Srivatsan Krishnan, Max Lam, Sharad Chitlangia, Zishen Wan, Gabriel Barth-Maron, Aleksandra Faust, Vijay Janapa Reddi

Trans. Mach. Learn. Res., 2022

2021

Launchpad: A Programming Model for Distributed Machine Learning Research.

Fan Yang, Gabriel Barth-Maron, Piotr Stanczyk, Matthew W. Hoffman, Siqi Liu, Manuel Kroiss, Aedan Pope, Alban Rrustemi

CoRR, 2021

Reverb: A Framework For Experience Replay.

Albin Cassirer, Gabriel Barth-Maron, Eugene Brevdo, Sabela Ramos, Toby Boyd, Thibault Sottiaux, Manuel Kroiss

CoRR, 2021

2020

Acme: A Research Framework for Distributed Reinforcement Learning.

Matt Hoffman, Bobak Shahriari, John Aslanides, Gabriel Barth-Maron, Feryal M. P. Behbahani, Tamara Norman, Abbas Abdolmaleki, Albin Cassirer, Fan Yang, Kate Baumli, Sarah Henderson, Alexander Novikov, Sergio Gómez Colmenarejo, Serkan Cabi, Çaglar Gülçehre, Tom Le Paine, Andrew Cowie, Ziyu Wang, Bilal Piot, Nando de Freitas

CoRR, 2020

Making Efficient Use of Demonstrations to Solve Hard Exploration Problems.

Çaglar Gülçehre, Tom Le Paine, Bobak Shahriari, Misha Denil, Matt Hoffman, Hubert Soyer, Richard Tanburn, Steven Kapturowski, Neil C. Rabinowitz, Duncan Williams, Gabriel Barth-Maron, Ziyu Wang, Nando de Freitas, Worlds Team

Proceedings of the 8th International Conference on Learning Representations, 2020

2018

One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL.

Tom Le Paine, Sergio Gomez Colmenarejo, Ziyu Wang, Scott E. Reed, Yusuf Aytar, Tobias Pfaff, Matthew W. Hoffman, Gabriel Barth-Maron, Serkan Cabi, David Budden, Nando de Freitas

CoRR, 2018

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, Olivier Pietquin

CoRR, 2018

Distributed Prioritized Experience Replay.

Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron, Matteo Hessel, Hado van Hasselt, David Silver

Proceedings of the 6th International Conference on Learning Representations, 2018

Distributed Distributional Deterministic Policy Gradients.

Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, Timothy P. Lillicrap

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Data-efficient Deep Reinforcement Learning for Dexterous Manipulation.

Ivaylo Popov, Nicolas Heess, Timothy P. Lillicrap, Roland Hafner, Gabriel Barth-Maron, Matej Vecerík, Thomas Lampe, Yuval Tassa, Tom Erez, Martin A. Riedmiller

CoRR, 2017

2015

Goal-Based Action Priors.

David Abel, D. Ellis Hershkowitz, Gabriel Barth-Maron, Stephen Brawner, Kevin O'Farrell, James MacGlashan, Stefanie Tellex

Proceedings of the Twenty-Fifth International Conference on Automated Planning and Scheduling, 2015

2014

Affordances as Transferable Knowledge for Planning Agents.

Gabriel Barth-Maron, David Abel, James MacGlashan, Stefanie Tellex

Proceedings of the 2014 AAAI Fall Symposia, Arlington, Virginia, USA, November 13-15, 2014, 2014