| 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 | 852 | 2018 |
| Gaussian process behaviour in wide deep neural networks AGG Matthews, M Rowland, J Hron, RE Turner, Z Ghahramani arXiv preprint arXiv:1804.11271, 2018 | 571* | 2018 |
| Revisiting fundamentals of experience replay W Fedus, P Ramachandran, R Agarwal, Y Bengio, H Larochelle, ... International conference on machine learning, 3061-3071, 2020 | 299 | 2020 |
| Black-box -divergence Minimization JM Hernández-Lobato, Y Li, M Rowland, D Hernández-Lobato, T Bui, ... arXiv preprint arXiv:1511.03243, 2015 | 272 | 2015 |
| A general theoretical paradigm to understand learning from human preferences MG Azar, ZD Guo, B Piot, R Munos, M Rowland, M Valko, D Calandriello International Conference on Artificial Intelligence and Statistics, 4447-4455, 2024 | 211 | 2024 |
| Mastering the game of Stratego with model-free multiagent reinforcement learning J Perolat, B De Vylder, D Hennes, E Tarassov, F Strub, V de Boer, ... Science 378 (6623), 990-996, 2022 | 199 | 2022 |
| Structured evolution with compact architectures for scalable policy optimization K Choromanski, M Rowland, V Sindhwani, R Turner, A Weller International Conference on Machine Learning, 970-978, 2018 | 147 | 2018 |
| α-Rank: Multi-Agent Evaluation by Evolution S Omidshafiei, C Papadimitriou, G Piliouras, K Tuyls, M Rowland, ... Scientific reports 9 (1), 9937, 2019 | 139 | 2019 |
| 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 | 135 | 2018 |
| Distributional reinforcement learning MG Bellemare, W Dabney, M Rowland MIT Press, 2023 | 134 | 2023 |
| A generalized training approach for multiagent learning P Muller, S Omidshafiei, M Rowland, K Tuyls, J Perolat, S Liu, D Hennes, ... arXiv preprint arXiv:1909.12823, 2019 | 113 | 2019 |
| Game Plan: What AI can do for Football, and What Football can do for AI K Tuyls, S Omidshafiei, P Muller, Z Wang, J Connor, D Hennes, I Graham, ... Journal of Artificial Intelligence Research 71, 41-88, 2021 | 101 | 2021 |
| Statistics and samples in distributional reinforcement learning M Rowland, R Dadashi, S Kumar, R Munos, MG Bellemare, W Dabney International Conference on Machine Learning, 5528-5536, 2019 | 98 | 2019 |
| The unreasonable effectiveness of structured random orthogonal embeddings KM Choromanski, M Rowland, A Weller Advances in neural information processing systems 30, 2017 | 97 | 2017 |
| Meta-learning of sequential strategies PA Ortega, JX Wang, M Rowland, T Genewein, Z Kurth-Nelson, ... arXiv preprint arXiv:1905.03030, 2019 | 96 | 2019 |
| Understanding and preventing capacity loss in reinforcement learning C Lyle, M Rowland, W Dabney arXiv preprint arXiv:2204.09560, 2022 | 90 | 2022 |
| From poincaré recurrence to convergence in imperfect information games: Finding equilibrium via regularization J Perolat, R Munos, JB Lespiau, S Omidshafiei, M Rowland, P Ortega, ... International Conference on Machine Learning, 8525-8535, 2021 | 85 | 2021 |
| On the effect of auxiliary tasks on representation dynamics C Lyle, M Rowland, G Ostrovski, W Dabney International Conference on Artificial Intelligence and Statistics, 1-9, 2021 | 74 | 2021 |
| The value-improvement path: Towards better representations for reinforcement learning W Dabney, A Barreto, M Rowland, R Dadashi, J Quan, MG Bellemare, ... Proceedings of the AAAI Conference on Artificial Intelligence 35 (8), 7160-7168, 2021 | 71 | 2021 |
| Nash learning from human feedback R Munos, M Valko, D Calandriello, MG Azar, M Rowland, ZD Guo, Y Tang, ... arXiv preprint arXiv:2312.00886, 2023 | 61 | 2023 |
Rémi MunosGoogle DeepMindVerified email at inria.fr
