| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 3308 | 2023 |
| A generalist agent S Reed, K Zolna, E Parisotto, SG Colmenarejo, A Novikov, G Barth-Maron, ... arXiv preprint arXiv:2205.06175, 2022 | 1019 | 2022 |
| Distributed distributional deterministic policy gradients G Barth-Maron, MW Hoffman, D Budden, W Dabney, D Horgan, D Tb, ... arXiv preprint arXiv:1804.08617, 2018 | 700 | 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 | 337 | 2017 |
| Acme: A research framework for distributed reinforcement learning MW Hoffman, B Shahriari, J Aslanides, G Barth-Maron, N Momchev, ... arXiv preprint arXiv:2006.00979, 2020 | 273 | 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 | 146 | 2018 |
| 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 | 101 | 2019 |
| 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 | 59 | 2015 |
| QuaRL: Quantization for fast and environmentally sustainable reinforcement learning S Krishnan, M Lam, S Chitlangia, Z Wan, G Barth-Maron, A Faust, ... arXiv preprint arXiv:1910.01055, 2019 | 33 | 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 | 30 | 2018 |
| 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 | 26 | 2021 |
| 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 | 21 | 2021 |
| Toward affordance-aware planning D Abel, G Barth-Maron, J MacGlashan, S Tellex First Workshop on Affordances: Affordances in Vision for Cognitive Robotics, 2014 | 16 | 2014 |
| 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 | 15 | 2020 |
| Reinforcement learning using distributed prioritized replay D Budden, G Barth-Maron, J Quan, DG Horgan US Patent 11,625,604, 2023 | 11 | 2023 |
| Distributional reinforcement learning for continuous control tasks D Budden, MW Hoffman, G Barth-Maron US Patent 11,481,629, 2022 | 10 | 2022 |
| 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 | 10 | 2017 |
| Affordances as Transferable Knowledge for Planning Agents. G Barth-Maron, D Abel, J MacGlashan, S Tellex AAAI Fall Symposia, 2014 | 7 | 2014 |
| Quantized reinforcement learning (quarl) Z Wan | 6 | 2019 |
| Learning deep state representations with convolutional autoencoders G Barth-Maron PhD thesis, Master’s thesis, Brown University, 2015 | 4 | 2015 |
Matthew W. HoffmanGoogle DeepMindכתובת אימייל מאומתת בדומיין google.com