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

A. Cully, J. Clune, D. Tarapore, and J. Mouret, Robots that can adapt like animals, Nature, vol.26, issue.7553, pp.503-507, 2015.
DOI : 10.1038/nrn2332
URL : https://hal.archives-ouvertes.fr/hal-01158243

K. Chatzilygeroudis, V. Vassiliades, and J. Mouret, Resetfree Trial-and-Error Learning for Robot Damage Recovery, 2016.
DOI : 10.1016/j.robot.2017.11.010
URL : https://hal.archives-ouvertes.fr/hal-01654641

J. Mouret, Micro-data learning: The other end of the spectrum, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01374786

M. P. Deisenroth, G. Neumann, and J. Peters, A Survey on Policy Search for Robotics, Foundations and Trends in Robotics, vol.2, issue.1-2, pp.1-142, 2013.
DOI : 10.1561/2300000021
URL : http://www.ias.tu-darmstadt.de/uploads/Publications/Deisenroth_ICRA_2014.pdf

M. Deisenroth, D. Fox, and C. Rasmussen, Gaussian Processes for Data-Efficient Learning in Robotics and Control, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.37, issue.2, pp.408-423
DOI : 10.1109/TPAMI.2013.218

K. Chatzilygeroudis, R. Rama, R. Kaushik, D. Goepp, V. Vassiliades et al., Black-box data-efficient policy search for robotics, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017.
DOI : 10.1109/IROS.2017.8202137
URL : https://hal.archives-ouvertes.fr/hal-01576683

M. B. Lizotte, T. Wang, and D. Schuurmans, Automatic Gait Optimization with Gaussian Process Regression, Proc. of IJCAI, 2007.

R. Calandra, A. Seyfarth, J. Peters, and M. P. Deisenroth, Bayesian optimization for learning gaits under uncertainty, Annals of Mathematics and Artificial Intelligence, vol.7, issue.1-2, pp.5-23, 2016.
DOI : 10.1088/1748-3182/7/3/036005
URL : http://spiral.imperial.ac.uk/bitstream/10044/1/24167/2/AMAI.pdf

D. R. Jones, M. Schonlau, and W. J. Welch, Efficient global optimization of expensive black-box functions, Journal of Global Optimization, vol.13, issue.4, pp.455-492, 1998.
DOI : 10.1023/A:1008306431147

B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. De-freitas, Taking the Human Out of the Loop: A Review of Bayesian Optimization, Proceedings of the IEEE, pp.148-175, 2016.
DOI : 10.1109/JPROC.2015.2494218

M. Cutler and J. P. How, Efficient reinforcement learning for robots using informative simulated priors, 2015 IEEE International Conference on Robotics and Automation (ICRA), 2015.
DOI : 10.1109/ICRA.2015.7139550
URL : http://dspace.mit.edu/bitstream/1721.1/109303/1/How_Efficient%20reinforcement.pdf

J. Ko, D. J. Klein, D. Fox, and D. Haehnel, Gaussian Processes and Reinforcement Learning for Identification and Control of an Autonomous Blimp, Proceedings 2007 IEEE International Conference on Robotics and Automation, 2007.
DOI : 10.1109/ROBOT.2007.363075

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

C. Plagemann, S. Mischke, S. Prentice, K. Kersting, N. Roy et al., Learning predictive terrain models for legged robot locomotion, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008.
DOI : 10.1109/IROS.2008.4651026
URL : http://www.informatik.uni-freiburg.de/~plagem/bib/plagemann08iros.pdf

K. Arulkumaran, M. P. Deisenroth, M. Brundage, and A. A. Bharath, Deep Reinforcement Learning: A Brief Survey, IEEE Signal Processing Magazine, vol.34, issue.6, 2017.
DOI : 10.1109/MSP.2017.2743240
URL : http://arxiv.org/pdf/1708.05866

R. S. Sutton, Learning to predict by the methods of temporal differences, Machine Learning, pp.9-44, 1988.
DOI : 10.3758/BF03205056

N. Kohl and P. Stone, Policy gradient reinforcement learning for fast quadrupedal locomotion, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004, 2004.
DOI : 10.1109/ROBOT.2004.1307456
URL : http://www.cs.utexas.edu/users/pstone/Papers/bib2html/../bib2html-links/icra04.ps

N. Hansen, A. Ostermeier, F. Stulp, and O. Sigaud, Completely derandomized self adaptation in evolution strategies Robot skill learning: From reinforcement learning to evolution strategies, Evolutionary Computation Paladyn, Journal of Behavioral Robotics, vol.20, issue.4 1, pp.159-195, 2001.

J. Schulman, S. Levine, P. Moritz, M. I. Jordan, and P. Abbeel, Trust region policy optimization, Proc. of ICML, 2015.

T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez et al., Continuous control with deep reinforcement learning, 2015.

A. S. Polydoros and L. Nalpantidis, Survey of Model-Based Reinforcement Learning: Applications on Robotics, Journal of Intelligent & Robotic Systems, vol.84, issue.3, pp.1-21, 2017.
DOI : 10.1109/IROS.2015.7353857

V. M. Brochu and N. De-freitas, A tutorial on bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning, 1012.

E. Rasmussen and C. K. Williams, Gaussian Processes in Machine Learning, 2006.
DOI : 10.1162/089976602317250933

P. Hennig and C. J. Schuler, Entropy search for information-efficient global optimization, Journal of Machine Learning Research, vol.13, 2011.

F. Berkenkamp, A. P. Schoellig, and A. Krause, Safe controller optimization for quadrotors with Gaussian processes, 2016 IEEE International Conference on Robotics and Automation (ICRA), 2016.
DOI : 10.1109/ICRA.2016.7487170

J. Rieffel and J. Mouret, Soft tensegrity robots, 2017.

V. Papaspyros, K. Chatzilygeroudis, V. Vassiliades, and J. Mouret, Safety-aware robot damage recovery using constrained bayesian optimization and simulated priors, BayesOpt '16 Workshop at NIPS, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01407757

A. Marco, F. Berkenkamp, P. Hennig, A. P. Schoellig, A. Krause et al., Virtual vs. real: Trading off simulations and physical experiments in reinforcement learning with Bayesian optimization, 2017 IEEE International Conference on Robotics and Automation (ICRA), 2017.
DOI : 10.1109/ICRA.2017.7989186

R. Antonova, A. Rai, and C. G. Atkeson, Deep Kernels for Optimizing Locomotion Controllers, Proc. of CoRL, 2017.

G. Lee, S. S. Srinivasa, and M. T. Mason, GP-ILQG: Data-driven Robust Optimal Control for Uncertain Nonlinear Dynamical Systems, 2017.

M. Saveriano, Y. Yin, P. Falco, and D. Lee, Data-efficient control policy search using residual dynamics learning, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017.
DOI : 10.1109/IROS.2017.8206343

A. Cully, K. Chatzilygeroudis, F. Allocati, and J. Mouret, Limbo: A fast and flexible library for bayesian optimization, 2016.

D. Lizotte, T. Wang, M. Bowling, and D. Schuurmansdepartment, Gaussian process regression for optimization, NIPS Workshop on Value of Information, 2005.

J. Mouret and J. Clune, Illuminating search spaces by mapping elites, 2015.

J. K. Pugh, L. B. Soros, and K. O. Stanley, Quality Diversity: A New Frontier for Evolutionary Computation, Frontiers in Robotics and AI, p.40, 2016.
DOI : 10.1007/s10846-011-9542-z

M. Duarte, J. Gomes, S. M. Oliveira, and A. L. Christensen, Evolution of Repertoire-Based Control for Robots With Complex Locomotor Systems, IEEE Transactions on Evolutionary Computation, vol.22, issue.2, 2017.
DOI : 10.1109/TEVC.2017.2722101

A. Gaier, A. Asteroth, and J. Mouret, Feature space modeling through surrogate illumination, Proc. of GECCO, 2017.

A. Nguyen, J. Yosinski, and J. Clune, Deep neural networks are easily fooled: High confidence predictions for unrecognizable images, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2015.7298640

V. Vassiliades, K. Chatzilygeroudis, and J. Mouret, Using Centroidal Voronoi Tessellations to Scale Up the Multi-dimensional Archive of Phenotypic Elites Algorithm, IEEE Transactions on Evolutionary Computation, 2017.
DOI : 10.1109/TEVC.2017.2735550
URL : https://hal.archives-ouvertes.fr/hal-01630627

J. Mouret and S. Doncieux, Sferesv2: Evolvin' in the Multi-Core World, Proc. of CEC, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00687633

M. Blum and M. A. Riedmiller, Optimization of Gaussian process hyperparameters using Rprop, Proc. of ESANN, 2013.

R. A. Brooks, Intelligence without representation, Artificial Intelligence, vol.47, issue.1-3, pp.139-159, 1991.
DOI : 10.1016/0004-3702(91)90053-M
URL : http://www.ai.mit.edu/people/jimmylin/papers/Brooks91.ps

R. Pfeifer and J. Bongard, How the body shapes the way we think: a new view of intelligence, 2006.