| Human-level control through deep reinforcement learning V Mnih, K Kavukcuoglu, D Silver, AA Rusu, J Veness, MG Bellemare, ... Nature 518 (7540), 529, 2015 | 6105 | 2015 |
| The Arcade Learning Environment: An Evaluation Platform for General Agents MG Bellemare, Y Naddaf, J Veness, M Bowling Journal of Artificial Intelligence Research 47, 253--279, 2013 | 924 | 2013 |
| Unifying count-based exploration and intrinsic motivation M Bellemare, S Srinivasan, G Ostrovski, T Schaul, D Saxton, R Munos Advances in Neural Information Processing Systems, 1471-1479, 2016 | 334 | 2016 |
| Safe and efficient off-policy reinforcement learning R Munos, T Stepleton, A Harutyunyan, M Bellemare Advances in Neural Information Processing Systems, 1054-1062, 2016 | 178 | 2016 |
| A distributional perspective on reinforcement learning MG Bellemare, W Dabney, R Munos Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017 | 162 | 2017 |
| Count-based exploration with neural density models G Ostrovski, MG Bellemare, A van den Oord, R Munos Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017 | 117 | 2017 |
| Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents MC Machado, MG Bellemare, E Talvitie, J Veness, M Hausknecht, ... Journal of Artificial Intelligence Research 61, 523-562, 2018 | 94 | 2018 |
| Automated curriculum learning for neural networks A Graves, MG Bellemare, J Menick, R Munos, K Kavukcuoglu Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017 | 89 | 2017 |
| The cramer distance as a solution to biased wasserstein gradients MG Bellemare, I Danihelka, W Dabney, S Mohamed, ... arXiv preprint arXiv:1705.10743, 2017 | 69 | 2017 |
| A laplacian framework for option discovery in reinforcement learning MC Machado, MG Bellemare, M Bowling Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017 | 65 | 2017 |
| Increasing the action gap: New operators for reinforcement learning MG Bellemare, G Ostrovski, A Guez, PS Thomas, R Munos Thirtieth AAAI Conference on Artificial Intelligence, 2016 | 54 | 2016 |
| Constructing evidence-based treatment strategies using methods from computer science J Pineau, MG Bellemare, AJ Rush, A Ghizaru, SA Murphy Drug and alcohol dependence 88, S52-S60, 2007 | 54 | 2007 |
| Investigating contingency awareness using Atari 2600 games MG Bellemare, J Veness, M Bowling Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012 | 53 | 2012 |
| Distributional reinforcement learning with quantile regression W Dabney, M Rowland, MG Bellemare, R Munos Thirty-Second AAAI Conference on Artificial Intelligence, 2018 | 40 | 2018 |
| The reactor: A sample-efficient actor-critic architecture A Gruslys, MG Azar, MG Bellemare, R Munos arXiv preprint arXiv:1704.04651 5, 2017 | 37 | 2017 |
| Q() with Off-Policy Corrections A Harutyunyan, MG Bellemare, T Stepleton, R Munos International Conference on Algorithmic Learning Theory, 305-320, 2016 | 30 | 2016 |
| A primer on reinforcement learning in the brain: Psychological, computational, and neural perspectives EA Ludvig, MG Bellemare, KG Pearson Computational neuroscience for advancing artificial intelligence: Models …, 2011 | 25 | 2011 |
| Bayesian Learning of Recursively Factored Environments M Bellemare, J Veness, M Bowling | 24 | 2013 |
| Skip context tree switching M Bellemare, J Veness, E Talvitie International Conference on Machine Learning, 1458-1466, 2014 | 22 | 2014 |
| Sketch-based linear value function approximation M Bellemare, J Veness, M Bowling Advances in Neural Information Processing Systems, 2213-2221, 2012 | 22 | 2012 |
Joel VenessGoogle DeepMindVerified email at google.com