| Meta-dataset: A dataset of datasets for learning to learn from few examples E Triantafillou, T Zhu, V Dumoulin, P Lamblin, U Evci, K Xu, R Goroshin, ... arXiv preprint arXiv:1903.03096, 2019 | 430 | 2019 |
| Dopamine: A research framework for deep reinforcement learning PS Castro, S Moitra, C Gelada, S Kumar, MG Bellemare arXiv preprint arXiv:1812.06110, 2018 | 225 | 2018 |
| DeepMDP: Learning Continuous Latent Space Models for Representation Learning C Gelada, S Kumar, J Buckman, O Nachum, MG Bellemare arXiv preprint arXiv:1906.02736, 2019 | 184 | 2019 |
| The importance of pessimism in fixed-dataset policy optimization J Buckman, C Gelada, MG Bellemare arXiv preprint arXiv:2009.06799, 2020 | 91 | 2020 |
| Off-policy deep reinforcement learning by bootstrapping the covariate shift C Gelada, MG Bellemare Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 3647-3655, 2019 | 87 | 2019 |
| Hyperbolic discounting and learning over multiple horizons W Fedus, C Gelada, Y Bengio, MG Bellemare, H Larochelle arXiv preprint arXiv:1902.06865, 2019 | 76 | 2019 |
| Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples. September 2019 E Triantafillou, T Zhu, V Dumoulin, P Lamblin, U Evci, K Xu, R Goroshin, ... URL https://openreview. net/forum, 0 | 2 | |
| Dopamine: A framework for flexible Reinforcement Learning research PS Castro, S Moitra, C Gelada, S Kumar, MG Bellemare | | |
Marc G. BellemareGoogle BrainVerified email at google.com
