| 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 | 19608 | 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 | 2443 | 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 29, 2016 | 1063 | 2016 |
| A distributional perspective on reinforcement learning MG Bellemare, W Dabney, R Munos International Conference on Machine Learning, 449-458, 2017 | 983 | 2017 |
| An introduction to deep reinforcement learning V François-Lavet, P Henderson, R Islam, MG Bellemare, J Pineau Foundations and Trends® in Machine Learning 11 (3-4), 219-354, 2018 | 728 | 2018 |
| Safe and efficient off-policy reinforcement learning R Munos, T Stepleton, A Harutyunyan, M Bellemare Advances in neural information processing systems 29, 2016 | 517 | 2016 |
| Count-based exploration with neural density models G Ostrovski, MG Bellemare, A Oord, R Munos International conference on machine learning, 2721-2730, 2017 | 444 | 2017 |
| Automated curriculum learning for neural networks A Graves, MG Bellemare, J Menick, R Munos, K Kavukcuoglu international conference on machine learning, 1311-1320, 2017 | 388 | 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 | 387 | 2018 |
| 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 | 361 | 2018 |
| The cramer distance as a solution to biased wasserstein gradients MG Bellemare, I Danihelka, W Dabney, S Mohamed, ... arXiv preprint arXiv:1705.10743, 2017 | 292 | 2017 |
| A laplacian framework for option discovery in reinforcement learning MC Machado, MG Bellemare, M Bowling International Conference on Machine Learning, 2295-2304, 2017 | 202 | 2017 |
| 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 | 181 | 2020 |
| Dopamine: A research framework for deep reinforcement learning PS Castro, S Moitra, C Gelada, S Kumar, MG Bellemare arXiv preprint arXiv:1812.06110, 2018 | 175 | 2018 |
| Deepmdp: Learning continuous latent space models for representation learning C Gelada, S Kumar, J Buckman, O Nachum, MG Bellemare International Conference on Machine Learning, 2170-2179, 2019 | 138 | 2019 |
| 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 | 126 | 2016 |
| Autonomous navigation of stratospheric balloons using reinforcement learning MG Bellemare, S Candido, PS Castro, J Gong, MC Machado, S Moitra, ... Nature 588 (7836), 77-82, 2020 | 105 | 2020 |
| Count-based exploration with the successor representation MC Machado, MG Bellemare, M Bowling Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5125-5133, 2020 | 91 | 2020 |
| Investigating contingency awareness using Atari 2600 games MG Bellemare, J Veness, M Bowling Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012 | 83 | 2012 |
| An analysis of categorical distributional reinforcement learning M Rowland, M Bellemare, W Dabney, R Munos, YW Teh International Conference on Artificial Intelligence and Statistics, 29-37, 2018 | 79 | 2018 |
Joel VenessGoogle DeepMindGeverifieerd e-mailadres voor google.com
