A. Argyriou, T. Evgeniou, and M. Pontil, Convex multi-task feature learning, Machine Learning, pp.243-272, 2008.
DOI : 10.1007/s10994-007-5040-8

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.130.2025

A. Argyriou, A. Charles, M. Micchelli, and . Pontil, Learning Convex Combinations of Continuously Parameterized Basic Kernels, Learning Theory, pp.338-352, 2005.
DOI : 10.1007/11503415_23

D. Bertsekas and J. Tsitsiklis, Neuro-Dynamic Programming, Athena Scientific, 1996.

J. Peter, . Bickel, . Ritov, and . Tsybakov, Simultaneous analysis of lasso and dantzig selector. The Annals of Statistics, pp.1705-1732, 2009.

P. Bühlmann and S. Van-de-geer, Statistics for High-Dimensional Data: Methods, Theory and Applications, 2011.
DOI : 10.1007/978-3-642-20192-9

A. Castelletti, . Galelli, R. Restelli, and . Soncini-sessa, Tree-based feature selection for dimensionality reduction of large-scale control systems, 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, pp.11-15, 2011.

D. Ernst, P. Geurts, L. Wehenkel, L. Michael, and . Littman, Tree-based batch mode reinforcement learning, Journal of Machine Learning Research, vol.6, issue.4, 2005.

R. Amir-massoud-farahmand, C. Munos, and . Szepesvári, Error propagation for approximate policy and value iteration, NIPS, pp.568-576, 2010.

M. Ghavamzadeh, A. Lazaric, R. Munos, and M. Hoffman, Finite-sample analysis of lasso-td, International Conference on Machine Learning, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00830149

H. Hachiya and M. Sugiyama, Feature Selection for Reinforcement Learning: Evaluating Implicit State-Reward Dependency via Conditional Mutual Information, Machine Learning and Knowledge Discovery in Databases, 2010.
DOI : 10.1007/978-3-642-15880-3_36

T. Hastie, R. Tibshirani, and J. Friedman, The elements of statistical learning, 2009.

M. Hoffman, A. Lazaric, M. Ghavamzadeh, and R. Munos, Regularized Least Squares Temporal Difference Learning with Nested ???2 and ???1 Penalization, EWRL, pp.102-114
DOI : 10.1007/978-3-642-29946-9_13

L. Jacob, G. Obozinski, and J. Vert, Group lasso with overlap and graph lasso, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.433-440, 2009.
DOI : 10.1145/1553374.1553431

J. Zico, K. Andrew, and Y. Ng, Regularization and feature selection in least-squares temporal difference learning, Proceedings of the 26th annual international conference on machine learning, 2009.

A. Lazaric, Transfer in Reinforcement Learning: A Framework and a Survey, Reinforcement Learning: State of the Art, 2011.
DOI : 10.1007/978-3-642-27645-3_5

URL : https://hal.archives-ouvertes.fr/hal-00772626

A. Lazaric and M. Ghavamzadeh, Bayesian multi-task reinforcement learning, Proceedings of the Twenty-Seventh International Conference on Machine Learning, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00475214

A. Lazaric and M. Restelli, Transfer from multiple MDPs, Proceedings of the Twenty- Fifth Annual Conference on Neural Information Processing Systems (NIPS'11), 2011.
URL : https://hal.archives-ouvertes.fr/hal-00772620

H. Li, X. Liao, and L. Carin, Multi-task reinforcement learning in partially observable stochastic environments, Journal of Machine Learning Research, vol.10, pp.1131-1186, 2009.

K. Lounici, M. Pontil, S. Van-de-geer, and A. B. Tsybakov, Oracle inequalities and optimal inference under group sparsity. The Annals of Statistics, pp.2164-2204, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00501509

A. Charles, J. Micchelli, M. Morales, and . Pontil, A family of penalty functions for structured sparsity, NIPS, pp.1612-1623, 2010.

R. Munos and C. Szepesvári, Finite-time bounds for fitted value iteration, The Journal of Machine Learning Research, vol.9, pp.815-857, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00120882

S. Negahban, J. Martin, and . Wainwright, Estimation of (near) low-rank matrices with noise and high-dimensional scaling. The Annals of Statistics, pp.1069-1097, 2011.

C. Painter-wakefield and R. Parr, Greedy algorithms for sparse reinforcement learning, ICML, 2012.

B. Scherrer, V. Gabillon, M. Ghavamzadeh, and M. Geist, Approximate modified policy iteration, Proceedings of the 29th International Conference on Machine Learning, ICML 2012, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00758882

M. Snel and S. Whiteson, Multi-Task Reinforcement Learning: Shaping and Feature Selection, Proceedings of the European Workshop on Reinforcement Learning (EWRL), 2011.
DOI : 10.1007/978-3-642-29946-9_24

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.231.6621

S. Richard, . Sutton, G. Andrew, and . Barto, Introduction to reinforcement learning, 1998.

F. Tanaka and M. Yamamura, Multitask reinforcement learning on the distribution of MDPs, Proceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium (Cat. No.03EX694), pp.1108-1113, 2003.
DOI : 10.1109/CIRA.2003.1222152

E. Matthew, P. Taylor, and . Stone, Transfer learning for reinforcement learning domains: A survey, Journal of Machine Learning Research, vol.10, issue.1, pp.1633-1685, 2009.

A. Sara, P. Van-de-geer, and . Bühlmann, On the conditions used to prove oracle results for the lasso, Electronic Journal of Statistics, vol.3, pp.1360-1392, 2009.

A. Wilson, A. Fern, S. Ray, and P. Tadepalli, Multi-task reinforcement learning, Proceedings of the 24th international conference on Machine learning, ICML '07, pp.1015-1022, 2007.
DOI : 10.1145/1273496.1273624

Y. Zhang and J. G. Schneider, Learning multiple tasks with a sparse matrix-normal penalty, NIPS, pp.2550-2558, 2010.