| Modification of UCT with patterns in Monte-Carlo Go S Gelly, Y Wang, O Teytaud, MU Patterns, P Tao | 468 | 2006 |
| 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 | 453 | 2016 |
| Exploration–exploitation tradeoff using variance estimates in multi-armed bandits JY Audibert, R Munos, C Szepesvári Theoretical Computer Science 410 (19), 1876-1902, 2009 | 430 | 2009 |
| Best arm identification in multi-armed bandits JY Audibert, S Bubeck | 405 | 2010 |
| Thompson sampling: An asymptotically optimal finite-time analysis E Kaufmann, N Korda, R Munos International conference on algorithmic learning theory, 199-213, 2012 | 372 | 2012 |
| Variable resolution discretization in optimal control R Munos, A Moore Machine learning 49 (2-3), 291-323, 2002 | 332 | 2002 |
| Pure exploration in multi-armed bandits problems S Bubeck, R Munos, G Stoltz International conference on Algorithmic learning theory, 23-37, 2009 | 288 | 2009 |
| Sample efficient actor-critic with experience replay Z Wang, V Bapst, N Heess, V Mnih, R Munos, K Kavukcuoglu, ... arXiv preprint arXiv:1611.01224, 2016 | 286 | 2016 |
| A distributional perspective on reinforcement learning MG Bellemare, W Dabney, R Munos Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017 | 279 | 2017 |
| X-armed bandits S Bubeck, R Munos, G Stoltz, C Szepesvári Journal of Machine Learning Research 12 (May), 1655-1695, 2011 | 277 | 2011 |
| Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures L Espeholt, H Soyer, R Munos, K Simonyan, V Mnih, T Ward, Y Doron, ... arXiv preprint arXiv:1802.01561, 2018 | 266 | 2018 |
| Safe and efficient off-policy reinforcement learning R Munos, T Stepleton, A Harutyunyan, M Bellemare Advances in Neural Information Processing Systems, 1054-1062, 2016 | 244 | 2016 |
| Learning to reinforcement learn JX Wang, Z Kurth-Nelson, D Tirumala, H Soyer, JZ Leibo, R Munos, ... arXiv preprint arXiv:1611.05763, 2016 | 243 | 2016 |
| Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path A Antos, C Szepesvári, R Munos Machine Learning 71 (1), 89-129, 2008 | 239 | 2008 |
| Finite-time bounds for fitted value iteration R Munos, C Szepesvári Journal of Machine Learning Research 9 (May), 815-857, 2008 | 226 | 2008 |
| Gaussian process dynamic programming MP Deisenroth, CE Rasmussen, J Peters Neurocomputing 72 (7-9), 1508-1524, 2009 | 219 | 2009 |
| Kullback–leibler upper confidence bounds for optimal sequential allocation O Cappé, A Garivier, OA Maillard, R Munos, G Stoltz The Annals of Statistics 41 (3), 1516-1541, 2013 | 212 | 2013 |
| Bandit algorithms for tree search PA Coquelin, R Munos arXiv preprint cs/0703062, 2007 | 209 | 2007 |
| Noisy networks for exploration M Fortunato, MG Azar, B Piot, J Menick, I Osband, A Graves, V Mnih, ... arXiv preprint arXiv:1706.10295, 2017 | 207 | 2017 |
| Online optimization in X-armed bandits S Bubeck, G Stoltz, C Szepesvári, R Munos Advances in Neural Information Processing Systems, 201-208, 2009 | 193 | 2009 |
Csaba SzepesvariDeepMind & Department of Computing Science, University of AlbertaVerified email at cs.ualberta.ca
