N. Heess, Emergence of locomotion behaviours in rich environments, 2017.

V. Mnih, Human-level control through deep reinforcement learning, Nature, vol.518, issue.7540, pp.529-533, 2015.

C. Finn, P. Abbeel, and S. Levine, Model-agnostic meta-learning for fast adaptation of deep networks, Proc. of ICML. JMLR. org, pp.1126-1135, 2017.

K. Chatzilygeroudis, V. Vassiliades, F. Stulp, S. Calinon, and J. Mouret, A survey on policy search algorithms for learning robot controllers in a handful of trials, IEEE Transactions on Robotics, vol.36, issue.2, pp.328-347, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02393432

M. P. Deisenroth and C. E. Rasmussen, PILCO: A model-based and data-efficient approach to policy search, Proc. of ICML, 2011.

K. Chatzilygeroudis, Black-Box Data-efficient Policy Search for Robotics, Proc. of IROS, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01576683

R. Kaushik, K. Chatzilygeroudis, and J. Mouret, Multi-objective model-based policy search for data-efficient learning with sparse rewards, Conference on Robot Learning, pp.839-855, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01884294

G. Williams, Information theoretic mpc for model-based reinforcement learning, Proc. of ICRA, 2017.

A. Nagabandi, Learning to adapt: Meta-learning for modelbased control, Proc. of ICLR, 2019.

K. Chua, Deep reinforcement learning in a handful of trials using probabilistic dynamics models, Proc. of NIPS, pp.4754-4765, 2018.

E. Keogh and A. Mueen, Encyclopedia of machine learning, pp.257-258, 2010.

A. Nagabandi, Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning, Proc. of ICRA, pp.7559-7566, 2018.

A. Cully, J. Clune, D. Tarapore, and J. Mouret, Robots that can adapt like animals, Nature, vol.521, issue.7553, pp.503-507, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01158243

R. Collobert, Natural language processing (almost) from scratch, JMLR, vol.12, pp.2493-2537, 2011.

J. Schulman, Proximal policy optimization algorithms, 2017.

J. Hollerbach, W. Khalil, and M. Gautier, Model Identification, pp.113-138, 2016.

M. Cutler and J. P. How, Efficient reinforcement learning for robots using informative simulated priors, Proc. of ICRA, 2015.

M. Saveriano, Y. Yin, P. Falco, and D. Lee, Data-efficient control policy search using residual dynamics learning, Proc. of IROS, 2017.

K. Chatzilygeroudis and J. Mouret, Using Parameterized Black-Box Priors to Scale Up Model-Based Policy Search for Robotics, Proc. of ICRA, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01768285

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, vol.3, p.40, 2016.

A. Cully and Y. Demiris, Quality and diversity optimization: A unifying modular framework, IEEE Trans. on Evolutionary Computation, vol.22, issue.2, pp.245-259, 2018.

A. Cully and J. Mouret, Evolving a behavioral repertoire for a walking robot, Evolutionary Computation, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01095543

K. Chatzilygeroudis, V. Vassiliades, and J. Mouret, Reset-free trial-and-error learning for robot damage recovery, Robotics and Autonomous Systems, vol.100, pp.236-250, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01654641

R. Kaushik, P. Desreumaux, and J. Mouret, Adaptive prior selection for repertoire-based online adaptation in robotics, Frontiers in Robotics and AI, vol.6, p.151, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02462935

R. Pautrat, K. Chatzilygeroudis, and J. Mouret, Bayesian optimization with automatic prior selection for data-efficient direct policy search, Proc. of ICRA, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01768279

C. F. Perez, F. P. Such, and T. Karaletsos, Efficient transfer learning and online adaptation with latent variable models for continuous control, 2018.

S. Saemundsson, K. Hofmann, and M. Deisenroth, Meta reinforcement learning with latent variable Gaussian processes, Conference on Uncertainty in Artificial Intelligence, vol.34, pp.642-652, 2018.

A. Nichol, J. Achiam, and J. Schulman, On first-order meta-learning algorithms, 2018.

A. V. Rao, A survey of numerical methods for optimal control, Advances in the Astronautical Sciences, vol.135, issue.1, pp.497-528, 2009.

Z. I. Botev, The cross-entropy method for optimization, Handbook of statistics, vol.31, pp.35-59, 2013.

E. Coumans, Bullet physics library, Open source: bulletphysics. org, vol.15, p.5, 2013.