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Rémi Munos Vis

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*2009
34EESébastien Bubeck, Rémi Munos, Gilles Stoltz: Pure Exploration in Multi-armed Bandits Problems. ALT 2009: 23-37
33EEJean-Yves Audibert, Peter Auer, Alessandro Lazaric, Rémi Munos, Daniil Ryabko, Csaba Szepesvári: Workshop summary: On-line learning with limited feedback. ICML 2009: 168
32EEJean-Yves Audibert, Rémi Munos, Csaba Szepesvári: Exploration-exploitation tradeoff using variance estimates in multi-armed bandits. Theor. Comput. Sci. 410(19): 1876-1902 (2009)
2008
31 Sertan Girgin, Manuel Loth, Rémi Munos, Philippe Preux, Daniil Ryabko: Recent Advances in Reinforcement Learning, 8th European Workshop, EWRL 2008, Villeneuve d'Ascq, France, June 30 - July 3, 2008, Revised and Selected Papers Springer 2008
30EERaphaël Maîtrepierre, Jérémie Mary, Rémi Munos: Adaptive play in Texas Hold'em Poker. ECAI 2008: 458-462
29EEJean-François Hren, Rémi Munos: Optimistic Planning of Deterministic Systems. EWRL 2008: 151-164
28EEYizao Wang, Jean-Yves Audibert, Rémi Munos: Algorithms for Infinitely Many-Armed Bandits. NIPS 2008: 1729-1736
27EESébastien Bubeck, Rémi Munos, Gilles Stoltz, Csaba Szepesvári: Online Optimization in X-Armed Bandits. NIPS 2008: 201-208
26EEPierre-Arnaud Coquelin, Romain Deguest, Rémi Munos: Particle Filter-based Policy Gradient in POMDPs. NIPS 2008: 337-344
25EESébastien Bubeck, Rémi Munos, Gilles Stoltz: Pure Exploration for Multi-Armed Bandit Problems CoRR abs/0802.2655: (2008)
24EEAndrás Antos, Csaba Szepesvári, Rémi Munos: Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path. Machine Learning 71(1): 89-129 (2008)
2007
23EEJean-Yves Audibert, Rémi Munos, Csaba Szepesvári: Tuning Bandit Algorithms in Stochastic Environments. ALT 2007: 150-165
22EEAndrás Antos, Rémi Munos, Csaba Szepesvári: Fitted Q-iteration in continuous action-space MDPs. NIPS 2007
21EEPierre-Arnaud Coquelin, Rémi Munos: Bandit Algorithms for Tree Search CoRR abs/cs/0703062: (2007)
20EERémi Munos: Analyse en norme Lp de l'algorithme d'itérations sur les valeurs avec approximations. Revue d'Intelligence Artificielle 21(1): 53-74 (2007)
2006
19EEAndrás Antos, Csaba Szepesvári, Rémi Munos: Learning Near-Optimal Policies with Bellman-Residual Minimization Based Fitted Policy Iteration and a Single Sample Path. COLT 2006: 574-588
18EERémi Munos: Geometric Variance Reduction in Markov Chains: Application to Value Function and Gradient Estimation. Journal of Machine Learning Research 7: 413-427 (2006)
17EERémi Munos: Policy Gradient in Continuous Time. Journal of Machine Learning Research 7: 771-791 (2006)
2005
16 Rémi Munos: Error Bounds for Approximate Value Iteration. AAAI 2005: 1006-1011
15 Rémi Munos: Geometric Variance Reduction in Markov Chains. Application to Value Function and Gradient Estimation. AAAI 2005: 1012-1017
14 Rémi Munos: Policy gradient in continuous time. CAP 2005: 201-216
13EECsaba Szepesvári, Rémi Munos: Finite time bounds for sampling based fitted value iteration. ICML 2005: 880-887
2003
12 Rémi Munos: Error Bounds for Approximate Policy Iteration. ICML 2003: 560-567
2002
11 Rémi Munos, Andrew W. Moore: Variable Resolution Discretization in Optimal Control. Machine Learning 49(2-3): 291-323 (2002)
2001
10EERémi Munos: Efficient Resources Allocation for Markov Decision Processes. NIPS 2001: 1571-1578
2000
9 Rémi Munos, Andrew W. Moore: Rates of Convergence for Variable Resolution Schemes in Optimal Control. ICML 2000: 647-654
8 Rémi Munos: A Study of Reinforcement Learning in the Continuous Case by the Means of Viscosity Solutions. Machine Learning 40(3): 265-299 (2000)
1999
7 Rémi Munos, Andrew W. Moore: Variable Resolution Discretization for High-Accuracy Solutions of Optimal Control Problems. IJCAI 1999: 1348-1355
1998
6EERémi Munos: A General Convergence Method for Reinforcement Learning in the Continuous Case. ECML 1998: 394-405
5EERémi Munos, Andrew W. Moore: Barycentric Interpolators for Continuous Space and Time Reinforcement Learning. NIPS 1998: 1024-1030
1997
4EERémi Munos: Finite-Element Methods with Local Triangulation Refinement for Continuous Reimforcement Learning Problems. ECML 1997: 170-182
3 Rémi Munos: A Convergent Reinforcement Learning Algorithm in the Continuous Case Based on a Finite Difference Method. IJCAI (2) 1997: 826-831
2 Rémi Munos, Paul Bourgine: Reinforcement Learning for Continuous Stochastic Control Problems. NIPS 1997
1996
1 Rémi Munos: A Convergent Reinforcement Learning Algorithm in the Continuous Case: The Finite-Element Reinforcement Learning. ICML 1996: 337-345

Coauthor Index

1András Antos [19] [22] [24]
2Jean-Yves Audibert [23] [28] [32] [33]
3Peter Auer [33]
4Paul Bourgine [2]
5Sébastien Bubeck [25] [27] [34]
6Pierre-Arnaud Coquelin [21] [26]
7Romain Deguest [26]
8Sertan Girgin [31]
9Jean-François Hren [29]
10Alessandro Lazaric [33]
11Manuel Loth [31]
12Raphaël Maîtrepierre [30]
13Jérémie Mary [30]
14Andrew W. Moore [5] [7] [9] [11]
15Philippe Preux [31]
16Daniil Ryabko [31] [33]
17Gilles Stoltz [25] [27] [34]
18Csaba Szepesvári [13] [19] [22] [23] [24] [27] [32] [33]
19Yizao Wang [28]

Colors in the list of coauthors

Copyright © Tue Nov 3 08:52:44 2009 by Michael Ley (ley@uni-trier.de)