Rémi Munos
Cited by
Cited by
Bootstrap your own latent-a new approach to self-supervised learning
JB Grill, F Strub, F Altché, C Tallec, P Richemond, E Buchatskaya, ...
Advances in neural information processing systems 33, 21271-21284, 2020
A distributional perspective on reinforcement learning
MG Bellemare, W Dabney, R Munos
International conference on machine learning, 449-458, 2017
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
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, ...
International conference on machine learning, 1407-1416, 2018
Learning to reinforcement learn
JX Wang, Z Kurth-Nelson, D Tirumala, H Soyer, JZ Leibo, R Munos, ...
arXiv preprint arXiv:1611.05763, 2016
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
Best arm identification in multi-armed bandits.
JY Audibert, S Bubeck, R Munos
COLT, 41-53, 2010
Minimax regret bounds for reinforcement learning
MG Azar, I Osband, R Munos
International Conference on Machine Learning, 263-272, 2017
Thompson sampling: An asymptotically optimal finite-time analysis
E Kaufmann, N Korda, R Munos
International conference on algorithmic learning theory, 199-213, 2012
Exploration–exploitation tradeoff using variance estimates in multi-armed bandits
JY Audibert, R Munos, C Szepesvári
Theoretical Computer Science 410 (19), 1876-1902, 2009
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
Count-based exploration with neural density models
G Ostrovski, MG Bellemare, A Oord, R Munos
International conference on machine learning, 2721-2730, 2017
Safe and efficient off-policy reinforcement learning
R Munos, T Stepleton, A Harutyunyan, M Bellemare
Advances in neural information processing systems 29, 2016
Noisy networks for exploration
M Fortunato, MG Azar, B Piot, J Menick, I Osband, A Graves, V Mnih, ...
arXiv preprint arXiv:1706.10295, 2017
Finite-Time Bounds for Fitted Value Iteration.
R Munos, C Szepesvári
Journal of Machine Learning Research 9 (5), 2008
Pure exploration in multi-armed bandits problems
S Bubeck, R Munos, G Stoltz
Algorithmic Learning Theory: 20th International Conference, ALT 2009, Porto …, 2009
Automated curriculum learning for neural networks
A Graves, MG Bellemare, J Menick, R Munos, K Kavukcuoglu
international conference on machine learning, 1311-1320, 2017
Successor features for transfer in reinforcement learning
A Barreto, W Dabney, R Munos, JJ Hunt, T Schaul, HP van Hasselt, ...
Advances in neural information processing systems 30, 2017
Modification of UCT with patterns in Monte-Carlo Go
S Gelly, Y Wang, R Munos, O Teytaud
INRIA, 2006
Implicit quantile networks for distributional reinforcement learning
W Dabney, G Ostrovski, D Silver, R Munos
International conference on machine learning, 1096-1105, 2018
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Articles 1–20