C. Watkins and P. Dayan, Q-learning, Machine Learning, vol.8, pp.279-292, 1992.

R. S. Sutton, D. Precup, and S. Singh, Between mdps and semi-mdps : A framework for temporal abstraction in reinforcement learning, Artificial Intelligence, vol.112, issue.1-2, pp.181-211, 1999.

M. R. James and S. Singh, Learning and discovery of predictive state representations in dynamical systems with reset, ICML 2004, pp.417-424, 2004.

R. Munos, Error bounds for approximate policy iteration, International Conference on Machine Learning ICML 2003, pp.560-567, 2003.

A. Mccallum, Reinforcement Learning with Selective Perception and Hidden State, 1996.

B. Ravindran and A. G. Barto, Smdp homomorphisms: An algebraic approach to abstraction in semi markov decision processes, IJCAI 2003, pp.1011-1016, 2003.

R. Givan, T. Dean, and M. Greig, Equivalence notions and model minimization in markov decision processes, Artificial Intelligence, vol.147, issue.1-2, pp.163-223, 2003.

T. G. Dietterich, State abstraction in maxq hierachical reinforcement learning, Artificial Intelligence Research, issue.13, pp.227-303, 2000.

S. Dzeroski, L. D. Raedt, and K. Driessens, Relational reinforcement learning, Machine Learning, issue.43, pp.7-52, 2001.

M. Ricordeau, Q-concept-learning: Generalization with concept lattice representation in reinforcement learning, pp.316-323, 2003.
URL : https://hal.archives-ouvertes.fr/lirmm-00269752

R. S. Sutton and A. G. Barto, Reinforcement Learning : An Introduction, 1998.

B. Ganter and R. Wille, Formal Concept Analysis: Mathematical Foundations, 1999.

T. M. Mitchell, Machine Learning, 1997.

M. Liquière and J. Sallantin, Structural machine learning with galois lattice and graphs, pp.305-313, 1998.