| Human-level control through deep reinforcement learning V Mnih, K Kavukcuoglu, D Silver, AA Rusu, J Veness, MG Bellemare, ... nature 518 (7540), 529-533, 2015 | 13059 | 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 | 1699 | 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 | 683 | 2016 |
| A distributional perspective on reinforcement learning MG Bellemare, W Dabney, R Munos arXiv preprint arXiv:1707.06887, 2017 | 545 | 2017 |
| Safe and efficient off-policy reinforcement learning R Munos, T Stepleton, A Harutyunyan, M Bellemare Advances in neural information processing systems 29, 1054-1062, 2016 | 347 | 2016 |
| An introduction to deep reinforcement learning V François-Lavet, P Henderson, R Islam, MG Bellemare, J Pineau arXiv preprint arXiv:1811.12560, 2018 | 284 | 2018 |
| Count-based exploration with neural density models G Ostrovski, MG Bellemare, A Oord, R Munos arXiv preprint arXiv:1703.01310, 2017 | 277 | 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 | 237 | 2018 |
| Automated curriculum learning for neural networks A Graves, MG Bellemare, J Menick, R Munos, K Kavukcuoglu arXiv preprint arXiv:1704.03003, 2017 | 223 | 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 | 169 | 2017 |
| Distributional reinforcement learning with quantile regression W Dabney, M Rowland, M Bellemare, R Munos Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 167 | 2018 |
| A laplacian framework for option discovery in reinforcement learning MC Machado, MG Bellemare, M Bowling arXiv preprint arXiv:1703.00956, 2017 | 130 | 2017 |
| Dopamine: A research framework for deep reinforcement learning PS Castro, S Moitra, C Gelada, S Kumar, MG Bellemare arXiv preprint arXiv:1812.06110, 2018 | 98 | 2018 |
| Increasing the action gap: New operators for reinforcement learning MG Bellemare, G Ostrovski, A Guez, P Thomas, R Munos Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016 | 95 | 2016 |
| The hanabi challenge: A new frontier for ai research N Bard, JN Foerster, S Chandar, N Burch, M Lanctot, HF Song, E Parisotto, ... Artificial Intelligence 280, 103216, 2020 | 75 | 2020 |
| Investigating contingency awareness using Atari 2600 games M Bellemare, J Veness, M Bowling Proceedings of the AAAI Conference on Artificial Intelligence 26 (1), 2012 | 72 | 2012 |
| 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 | 69 | 2007 |
| The reactor: A sample-efficient actor-critic architecture A Gruslys, MG Azar, MG Bellemare, R Munos arXiv preprint arXiv:1704.04651 5, 2017 | 54 | 2017 |
| Q() with Off-Policy Corrections A Harutyunyan, MG Bellemare, T Stepleton, R Munos International Conference on Algorithmic Learning Theory, 305-320, 2016 | 49 | 2016 |
| & Petersen, S.(2015). Human-level control through deep reinforcement learning V Mnih, K Kavukcuoglu, D Silver, AA Rusu, J Veness, MG Bellemare Nature 518 (7540), 529, 0 | 44 | |
Joel VenessGoogle DeepMindVerified email at google.com
