| The 2014 general video game playing competition D Perez-Liebana, S Samothrakis, J Togelius, T Schaul, SM Lucas, ... IEEE Transactions on Computational Intelligence and AI in Games 8 (3), 229-243, 2016 | 274 | 2016 |
| Continuous upper confidence trees A Couëtoux, JB Hoock, N Sokolovska, O Teytaud, N Bonnard International Conference on Learning and Intelligent Optimization, 433-445, 2011 | 242 | 2011 |
| Continuous upper confidence trees A Couëtoux, JB Hoock, N Sokolovska, O Teytaud, N Bonnard International Conference on Learning and Intelligent Optimization, 433-445, 2011 | 242 | 2011 |
| Continuous upper confidence trees with polynomial exploration–consistency D Auger, A Couëtoux, O Teytaud Machine Learning and Knowledge Discovery in Databases, 194-209, 2013 | 76 | 2013 |
| The 2016 Two-Player GVGAI Competition RD Gaina, A Couëtoux, DJNJ Soemers, MHM Winands, T Vodopivec, ... IEEE Transactions on Computational Intelligence and AI in Games, 2017 | 46 | 2017 |
| Monte carlo tree search for continuous and stochastic sequential decision making problems A Couetoux Université Paris Sud-Paris XI, 2013 | 33 | 2013 |
| Continuous rapid action value estimates A Couëtoux, M Milone, M Brendel, H Doghmen, M Sebag, O Teytaud Asian Conference on Machine Learning, 19-31, 2011 | 28 | 2011 |
| Benchmarking for bayesian reinforcement learning M Castronovo, D Ernst, A Couëtoux, R Fonteneau PloS one 11 (6), e0157088, 2016 | 14 | 2016 |
| Improving the exploration in upper confidence trees A Couetoux, H Doghmen, O Teytaud Learning and Intelligent Optimization, 366-371, 2012 | 14 | 2012 |
| Adding double progressive widening to upper confidence trees to cope with uncertainty in planning problems A Couëtoux, H Doghmen The 9th European Workshop on Reinforcement Learning (EWRL-9), 2011 | 11 | 2011 |
| Consistent belief state estimation, with application to mines A Couetoux, M Milone, O Teytaud Technologies and Applications of Artificial Intelligence (TAAI), 2011 …, 2011 | 8 | 2011 |
| Monte Carlo Tree Search in Go A Couëtoux, M Müller, O Teytaud | 7* | 2017 |
| Approximate Bayes Optimal Policy Search using Neural Networks M Castronovo, V François-Lavet, R Fonteneau, D Ernst, A Couëtoux Proceedings of the 9th International Conference on Agents and Artificial …, 2017 | 4 | 2017 |
| Combining policies: the best of human expertise and neurocontrol V Berthier, A Couëtoux, O Teytaud Evolutionary Algorithms 2015, 13, 2015 | 4 | 2015 |
| Strategic Choices in Optimization. CW Chou, PC Chou, JJ Christophe, A Couëtoux, P De Freminville, ... J. Inf. Sci. Eng. 30 (3), 727-747, 2014 | 4 | 2014 |
| Learning a move-generator for upper confidence trees A Couetoux, O Teytaud, H Doghmen Advances in Intelligent Systems and Applications-Volume 1, 209-218, 2013 | 2 | 2013 |
| Noisy optimization S Astete-Morales, ML Cauwet, A Couetoux, J Decock, J Liu, O Teytaud Dagstuhl seminar 13271, 2013 | 1 | 2013 |
| Monte Carlo Tree Search appliqué à la gestion de stocks A Couetoux, O Teytaud, N Bonnard, N Omont, O Ratier ROADEF 2011, N° 241, pI-149, 2011 | 1 | 2011 |
| Monte Carlo Tree Search for Continuous and Stochastic Sequential Decision Making Problems.(Monte Carlo Tree Search pour les problèmes de décision séquentielle en milieu … A Couëtoux University of Paris-Sud, Orsay, France, 2013 | | 2013 |
| Learning a Move-Generator for Upper Con dence Trees A Couetoux, O Teytaud, H Doghmen International Computer Symposium 2012, 2012 | | 2012 |
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