| Distributed prioritized experience replay D Horgan, J Quan, D Budden, G Barth-Maron, M Hessel, H Van Hasselt, ... arXiv preprint arXiv:1803.00933, 2018 | 547 | 2018 |
| Distributed distributional deterministic policy gradients G Barth-Maron, MW Hoffman, D Budden, W Dabney, D Horgan, D Tb, ... arXiv preprint arXiv:1804.08617, 2018 | 383 | 2018 |
| Data-efficient deep reinforcement learning for dexterous manipulation I Popov, N Heess, T Lillicrap, R Hafner, G Barth-Maron, M Vecerik, ... arXiv preprint arXiv:1704.03073, 2017 | 236 | 2017 |
| Acme: A research framework for distributed reinforcement learning M Hoffman, B Shahriari, J Aslanides, G Barth-Maron, F Behbahani, ... arXiv preprint arXiv:2006.00979, 2020 | 114 | 2020 |
| Observe and look further: Achieving consistent performance on atari T Pohlen, B Piot, T Hester, MG Azar, D Horgan, D Budden, G Barth-Maron, ... arXiv preprint arXiv:1805.11593, 2018 | 99 | 2018 |
| Goal-based action priors D Abel, D Hershkowitz, G Barth-Maron, S Brawner, K O'Farrell, ... Proceedings of the International Conference on Automated Planning and …, 2015 | 44 | 2015 |
| A generalist agent S Reed, K Zolna, E Parisotto, SG Colmenarejo, A Novikov, G Barth-Maron, ... arXiv preprint arXiv:2205.06175, 2022 | 23 | 2022 |
| Making efficient use of demonstrations to solve hard exploration problems TL Paine, C Gulcehre, B Shahriari, M Denil, M Hoffman, H Soyer, ... arXiv preprint arXiv:1909.01387, 2019 | 21 | 2019 |
| Making efficient use of demonstrations to solve hard exploration problems C Gulcehre, T Le Paine, B Shahriari, M Denil, M Hoffman, H Soyer, ... International conference on learning representations, 2019 | 18 | 2019 |
| One-shot high-fidelity imitation: Training large-scale deep nets with rl TL Paine, SG Colmenarejo, Z Wang, S Reed, Y Aytar, T Pfaff, ... arXiv preprint arXiv:1810.05017, 2018 | 18 | 2018 |
| Toward affordance-aware planning D Abel, G Barth-Maron, J MacGlashan, S Tellex First Workshop on Affordances: Affordances in Vision for Cognitive Robotics, 2014 | 10 | 2014 |
| Making efficient use of demonstrations to solve hard exploration problems T Le Paine, C Gulcehre, B Shahriari, M Denil, M Hoffman, H Soyer, ... arXiv e-prints, arXiv: 1909.01387, 2019 | 9 | 2019 |
| Reverb: a framework for experience replay A Cassirer, G Barth-Maron, E Brevdo, S Ramos, T Boyd, T Sottiaux, ... arXiv preprint arXiv:2102.04736, 2021 | 8 | 2021 |
| Diego de Las Casas, Andreas Fidjeland, Tim Green, Adrià Puigdomènech, Sébastien Racanière, Jack Rae, and Fabio Viola. Open sourcing Sonnet-a new library for constructing neural … M Reynolds, G Barth-Maron, F Besse | 8 | 2017 |
| Affordances as transferable knowledge for planning agents G Barth-Maron, D Abel, J MacGlashan, S Tellex 2014 AAAI Fall Symposium Series, 2014 | 7 | 2014 |
| Launchpad: a programming model for distributed machine learning research F Yang, G Barth-Maron, P Stańczyk, M Hoffman, S Liu, M Kroiss, A Pope, ... arXiv preprint arXiv:2106.04516, 2021 | 5 | 2021 |
| Data-efficient reinforcement learning for continuous control tasks M Riedmiller, R Hafner, M Vecerik, TP Lillicrap, T Lampe, I Popov, ... US Patent 10,664,725, 2020 | 4 | 2020 |
| Learning deep state representations with convolutional autoencoders G Barth-Maron PhD thesis, Master’s thesis, Brown University, 2015 | 4 | 2015 |
| Quantized reinforcement learning (quarl) M Lam, S Chitlangia, S Krishnan, Z Wan, G Barth-Maron, A Faust, ... arXiv preprint arXiv:1910.01055, 2019 | 3 | 2019 |
| Affordance-Aware Planning D Abel, G Barth-Maron, J MacGlashan, S Tellex | 2 | 2015 |
Dan HorganGoogle DeepMindDirección de correo verificada de google.com
