V. Mnih, K. Kavukcuoglu, D. Silver, A. Rusu-andrei, J. Veness et al., Human-level control through deep reinforcement learning, Nature, vol.101, issue.7540, pp.518529-533, 2015.
DOI : 10.1016/S0004-3702(98)00023-X

S. Levine, C. Finn, T. Darrell, and P. Abbeel, End-toend Training of Deep Visuomotor Policies, J. Mach. Learn. Res, vol.17, issue.1, pp.1334-1373, 2016.

V. Kumar, E. Todorov, and S. Levine, Optimal control with learned local models: Application to dexterous manipulation, 2016 IEEE International Conference on Robotics and Automation (ICRA), pp.378-383, 2016.
DOI : 10.1109/ICRA.2016.7487156

C. Finn, X. Y. Tan, Y. Duan, T. Darrell, S. Levine et al., Deep spatial autoencoders for visuomotor learning, 2016 IEEE International Conference on Robotics and Automation (ICRA), pp.512-519, 1509.
DOI : 10.1109/ICRA.2016.7487173

N. Heess, G. Wayne, D. Silver, T. P. Lillicrap, T. Erez et al., Learning Continuous Control Policies by Stochastic Value Gradients, NIPS, pp.2944-2952, 2015.

S. Gu, T. P. Lillicrap, I. Sutskever, and S. Levine, Continuous Deep Q-Learning with Model-based Acceleration, ICML Conference Proceedings, pp.2829-2838, 2016.

C. Finn and S. Levine, Deep visual foresight for planning robot motion, 2017 IEEE International Conference on Robotics and Automation (ICRA), 2016.
DOI : 10.1109/ICRA.2017.7989324

A. Ghadirzadeh, A. Maki, and M. Björkman, A sensorimotor approach for self-learning of hand-eye coordination, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp.4969-4975, 2015.
DOI : 10.1109/IROS.2015.7354076

L. Natale, F. Nori, G. Sandini, and G. Metta, Learning precise 3D reaching in a humanoid robot, 2007 IEEE 6th International Conference on Development and Learning, pp.324-329, 2007.
DOI : 10.1109/DEVLRN.2007.4354059

H. Hoffmann, W. Schenck, and R. Moller, Learning visuomotor transformations for gaze-control and grasping, Biological Cybernetics, vol.331, issue.1, pp.119-130, 2005.
DOI : 10.1016/B978-0-444-88400-8.50047-9

S. Hawkins, H. He, G. J. Williams, and R. A. Baxter, Outlier Detection Using Replicator Neural Networks, DaWaK, pp.170-180, 2002.
DOI : 10.1007/3-540-46145-0_17
URL : http://www.act.cmis.csiro.au/rohanb/PAPERS/dawak02.pdf

B. Schölkopf, R. C. Williamson, A. J. Smola, J. Shawe-taylor, and J. C. Platt, Support Vector Method for Novelty Detection, NIPS, pp.582-588, 1999.

A. Moreno, J. D. Martin, E. Soria, R. Magdalena, and M. Martinez, Noisy Reinforcements in reinforcement learning : some case studies based on gridworlds, WSEAS, pp.296-300, 2006.

R. Fox, A. Pakman, and N. Tishby, Taming the Noise in Reinforcement Learning via Soft Updates, UAI, 2016.

R. E. Bellman, Adaptive Control Processes : A Guided Tour, 1961.
DOI : 10.1515/9781400874668

R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, IEEE Transactions on Neural Networks, vol.9, issue.5, 1998.
DOI : 10.1109/TNN.1998.712192

S. Lange and M. Riedmiller, Deep auto-encoder neural networks in reinforcement learning, The 2010 International Joint Conference on Neural Networks (IJCNN), pp.1-8, 2010.
DOI : 10.1109/IJCNN.2010.5596468
URL : http://ml.informatik.uni-freiburg.de/_media/publications/langeijcnn2010.pdf

D. Silver, G. Lever, N. Heess, T. Degris, D. Wierstra et al., Deterministic Policy Gradient Algorithms, ICML, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00938992

M. J. Hausknecht and P. Stone, Deep Reinforcement Learning in Parameterized Action Space, 2015.