Human-level control through deep reinforcement learning, Nature, vol.518, issue.7540, pp.529-533, 2015. ,
Mastering the game of go with deep neural networks and tree search, Nature, vol.529, issue.7587, pp.484-489, 2016. ,
, Emergence of locomotion behaviours in rich environments, 2017.
PILCO: A model-based and data-efficient approach to policy search, Proc. of ICML, 2011. ,
Black-Box Data-efficient Policy Search for Robotics, Proc. of IROS, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01576683
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
Information theoretic mpc for model-based reinforcement learning, Proc. of ICRA, 2017. ,
Learning to adapt in dynamic, real-world environment through meta-reinforcement learning, Proc. of ICLR, 2019. ,
Deep reinforcement learning in a handful of trials using probabilistic dynamics models, Proc. of NIPS, pp.4754-4765, 2018. ,
Curse of dimensionality, Encyclopedia of Machine Learning and Data Mining, pp.314-315, 2017. ,
,
Evolving a behavioral repertoire for a walking robot, Evolutionary Computation, vol.24, issue.1, pp.59-88, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01095543
Robots that can adapt like animals, Nature, vol.521, issue.7553, pp.503-507, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01158243
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
Evolution of repertoire-based control for robots with complex locomotor systems, IEEE Transactions on Evolutionary Computation, vol.22, issue.2, pp.314-328, 2017. ,
Dynamics-aware unsupervised discovery of skills, 2019. ,
Illuminating search spaces by mapping elites, 2015. ,
Gaussian processes for machine learning, 2006. ,
A survey on policy search algorithms for learning robot controllers in a handful of trials, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-02393432
Gaussian processes for data-efficient learning in robotics and control, IEEE Trans. Pattern Anal. Mach. Intell, vol.37, issue.2, pp.408-423, 2015. ,
Model Identification, pp.113-138, 2016. ,
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
Efficient reinforcement learning for robots using informative simulated priors, Proc. of ICRA, 2015. ,
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
Bayesian optimization with automatic prior selection for data-efficient direct policy search, Proc. of ICRA, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01768279
Data-efficient control policy search using residual dynamics learning, Proc. of IROS, 2017. ,
A survey on policy search for robotics, Foundations and Trends in Robotics, vol.2, issue.1, pp.1-142, 2013. ,
Quality diversity: A new frontier for evolutionary computation, Frontiers in Robotics and AI, vol.3, p.40, 2016. ,
Quality and diversity optimization: A unifying modular framework, IEEE Trans. on Evolutionary Computation, vol.22, issue.2, pp.245-259, 2018. ,
Using centroidal voronoi tessellations to scale up the multidimensional archive of phenotypic elites algorithm, IEEE Transactions on Evolutionary Computation, vol.22, issue.4, pp.623-630, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01630627
Evolution of repertoire-based control for robots with complex locomotor systems, IEEE Transactions on Evolutionary Computation, vol.22, pp.314-328, 2018. ,
Reinforcement learning: An introduction, 1998. ,
Finite-time analysis of the multiarmed bandit problem, Machine learning, vol.47, issue.2-3, pp.235-256, 2002. ,
Bullet physics library, Open source: bulletphysics. org, vol.15, p.5, 2013. ,
GPy: A gaussian process framework in python ,
A unifying view of sparse approximate gaussian process regression, JMLR, vol.6, pp.1939-1959, 2005. ,
Patchwork kriging for large-scale gaussian process regression, 2017. ,
Dropout as a bayesian approximation: Representing model uncertainty in deep learning, Proc. of ICML, 2015. ,