| 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 | 39498 | 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 | 4330 | 2013 |
| A distributional perspective on reinforcement learning MG Bellemare, W Dabney, R Munos International conference on machine learning, 449-458, 2017 | 2404 | 2017 |
| An introduction to deep reinforcement learning FL Vincent, H Peter, I Riashat, GB Marc, P Joelle Foundations and trends® in machine learning 11 (3-4), 219-354, 2018 | 2344 | 2018 |
| 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 | 2042 | 2016 |
| 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 | 1200 | 2018 |
| Deep reinforcement learning at the edge of the statistical precipice R Agarwal, M Schwarzer, PS Castro, AC Courville, M Bellemare Advances in neural information processing systems 34, 29304-29320, 2021 | 1023 | 2021 |
| Count-based exploration with neural density models G Ostrovski, MG Bellemare, A Oord, R Munos International conference on machine learning, 2721-2730, 2017 | 862 | 2017 |
| Safe and efficient off-policy reinforcement learning R Munos, T Stepleton, A Harutyunyan, M Bellemare Advances in neural information processing systems 29, 2016 | 818 | 2016 |
| 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 | 771 | 2018 |
| Automated curriculum learning for neural networks A Graves, MG Bellemare, J Menick, R Munos, K Kavukcuoglu international conference on machine learning, 1311-1320, 2017 | 763 | 2017 |
| 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 | 546 | 2020 |
| 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 | 544 | 2020 |
| The cramer distance as a solution to biased wasserstein gradients MG Bellemare, I Danihelka, W Dabney, S Mohamed, ... arXiv preprint arXiv:1705.10743, 2017 | 511 | 2017 |
| 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 | 431 | 2019 |
| A laplacian framework for option discovery in reinforcement learning MC Machado, MG Bellemare, M Bowling International Conference on Machine Learning, 2295-2304, 2017 | 364 | 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 | 331 | 2018 |
| 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 | 271 | 2020 |
| Distributional reinforcement learning MG Bellemare, W Dabney, M Rowland MIT Press, 2023 | 261 | 2023 |
| Contrastive behavioral similarity embeddings for generalization in reinforcement learning R Agarwal, MC Machado, PS Castro, MG Bellemare arXiv preprint arXiv:2101.05265, 2021 | 249 | 2021 |
Joel VenessUniversal Artificial IntelligenceVerified email at godemperor.ai
