| Deep exploration via bootstrapped DQN I Osband, C Blundell, A Pritzel, B Van Roy Advances in neural information processing systems 29, 2016 | 1454 | 2016 |
| Deep q-learning from demonstrations T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, D Horgan, ... Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 1202 | 2018 |
| A tutorial on thompson sampling DJ Russo, B Van Roy, A Kazerouni, I Osband, Z Wen Foundations and Trends® in Machine Learning 11 (1), 1-96, 2018 | 1116 | 2018 |
| Minimax regret bounds for reinforcement learning MG Azar, I Osband, R Munos International conference on machine learning, 263-272, 2017 | 812 | 2017 |
| Randomized prior functions for deep reinforcement learning I Osband, J Aslanides, A Cassirer Advances in Neural Information Processing Systems 31, 2018 | 410 | 2018 |
| Deep Exploration via Randomized Value Functions I Osband https://searchworks.stanford.edu/view/11891201, 2016 | 328 | 2016 |
| Generalization and exploration via randomized value functions I Osband, B Van Roy, Z Wen International Conference on Machine Learning, 2377-2386, 2016 | 327 | 2016 |
| Why is posterior sampling better than optimism for reinforcement learning? I Osband, B Van Roy International conference on machine learning, 2701-2710, 2017 | 268 | 2017 |
| The uncertainty bellman equation and exploration B O’Donoghue, I Osband, R Munos, V Mnih International conference on machine learning, 3836-3845, 2018 | 215 | 2018 |
| Model-based reinforcement learning and the eluder dimension I Osband, B Van Roy Advances in Neural Information Processing Systems 27, 2014 | 189 | 2014 |
| Learning from demonstrations for real world reinforcement learning T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, A Sendonaris, ... arXiv preprint arXiv:1704.03732, 2017 | 178 | 2017 |
| Behaviour suite for reinforcement learning I Osband, Y Doron, M Hessel, J Aslanides, E Sezener, A Saraiva, ... arXiv preprint arXiv:1908.03568, 2019 | 174 | 2019 |
| Risk versus Uncertainty in Deep Learning: Bayes, Bootstrap and the Dangers of Dropout I Osband http://bayesiandeeplearning.org/papers/BDL_4.pdf, 0 | 166* | |
| Deep learning for time series modeling E Busseti, I Osband, S Wong Technical report, Stanford University, 1-5, 2012 | 139 | 2012 |
| Near-optimal reinforcement learning in factored mdps I Osband, B Van Roy Advances in Neural Information Processing Systems 27, 2014 | 121 | 2014 |
| On lower bounds for regret in reinforcement learning I Osband, B Van Roy arXiv preprint arXiv:1608.02732, 2016 | 112 | 2016 |
| Bootstrapped thompson sampling and deep exploration I Osband, B Van Roy arXiv preprint arXiv:1507.00300, 2015 | 100 | 2015 |
| (More) efficient reinforcement learning via posterior sampling I Osband, D Russo, B Van Roy Advances in Neural Information Processing Systems 26, 2013 | 100 | 2013 |
| Meta-learning of sequential strategies PA Ortega, JX Wang, M Rowland, T Genewein, Z Kurth-Nelson, ... arXiv preprint arXiv:1905.03030, 2019 | 87 | 2019 |
| Epistemic neural networks I Osband, Z Wen, SM Asghari, V Dwaracherla, M Ibrahimi, X Lu, ... Advances in Neural Information Processing Systems 36, 2024 | 86 | 2024 |
Benjamin Van RoyStanford University在 stanford.edu 的电子邮件经过验证
