Hindsight experience replay, Proc. of NIPS, 2017. ,
Unifying count-based exploration and intrinsic motivation, Proc. of NIPS, 2016. ,
Random forests. Machine learning, vol.45, pp.5-32, 2001. ,
Using Parameterized Black-Box Priors to Scale Up Model-Based Policy Search for Robotics, Proc. of ICRA, 2018. ,
DOI : 10.1109/icra.2018.8461083
URL : https://hal.archives-ouvertes.fr/hal-01768285
Vassilis Vassiliades, and Jean-Baptiste Mouret. Black-Box Data-efficient Policy Search for Robotics, Proc. of IROS, 2017. ,
GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms, Proc. of ICML, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01840576
Limbo: A Flexible Highperformance Library for Gaussian Processes modeling and Data-Efficient Optimization, The Journal of Open Source Software, vol.3, issue.26, p.545, 2018. ,
DOI : 10.21105/joss.00545
URL : https://hal.archives-ouvertes.fr/hal-01884299
Quality and diversity optimization: A unifying modular framework, IEEE Trans. on Evolutionary Computation, vol.22, issue.2, pp.245-259, 2018. ,
DOI : 10.1109/tevc.2017.2704781
URL : https://doi.org/10.1109/tevc.2017.2704781
Multi-Objective Optimization Using Evolutionary Algorithms, 2001. ,
A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. on Evolutionary Computation, vol.6, issue.2, pp.182-197, 2002. ,
DOI : 10.1109/4235.996017
URL : http://work.caltech.edu/amrit/papers/nsga2.ps.gz
Gaussian processes for dataefficient learning in robotics and control, IEEE Trans. Pattern Anal. Mach. Intell, vol.37, issue.2, pp.408-423, 2015. ,
A survey on policy search for robotics, Foundations and Trends in Robotics, vol.2, issue.1, pp.1-142, 2013. ,
Beyond black-box optimization: a review of selective pressures for evolutionary robotics, Evolutionary Intelligence, vol.7, issue.2, pp.71-93, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-01150254
Reverse curriculum generation for reinforcement learning, Conference on Robot Learning, 2017. ,
Intrinsically motivated goal exploration processes with automatic curriculum learning, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01651233
Curiosity-driven development of tool use precursors: a computational model, Proc. of COGSCI, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01354013
Simple genetic algorithms and the minimal, deceptive problem. Genetic algorithms and simulated annealing, pp.74-88, 1987. ,
Information-seeking, curiosity, and attention: computational and neural mechanisms, Trends in Cognitive Sciences, vol.17, issue.11, pp.585-593, 2013. ,
DOI : 10.1016/j.tics.2013.09.001
URL : https://hal.archives-ouvertes.fr/hal-00913646
The CMA Evolution Strategy: A Comparing Review, 2006. ,
DOI : 10.1007/11007937_4
URL : http://www.bionik.tu-berlin.de/user/niko/hansenedacomparing.pdf
, Emergence of locomotion behaviours in rich environments, 2017.
Vime: Variational information maximizing exploration, Proc. of NIPS, 2016. ,
Reinforcement learning: A survey, Journal of artificial intelligence research, vol.4, pp.237-285, 1996. ,
Near-optimal reinforcement learning in polynomial time, Machine learning, vol.49, issue.2-3, pp.209-232, 2002. ,
Policy search for motor primitives in robotics, Machine Learning, vol.84, pp.171-203, 2011. ,
Abandoning objectives: Evolution through the search for novelty alone, Evolutionary computation, vol.19, issue.2, pp.189-223, 2011. ,
Continuous control with deep reinforcement learning, Proc. of ICLR, 2016. ,
Exploration in modelbased reinforcement learning by empirically estimating learning progress, Proc. of NIPS, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-00755248
Human-level control through deep reinforcement learning, Nature, vol.518, issue.7540, pp.529-533, 2015. ,
Variational information maximisation for intrinsically motivated reinforcement learning, Proc. of NIPS, 2015. ,
Optimismdriven exploration for nonlinear systems, Proc. of ICRA, 2015. ,
Novelty-based Multiobjectivization, New Horizons in Evolutionary Robotics, pp.139-154, 2011. ,
DOI : 10.1007/978-3-642-18272-3_10
URL : https://hal.archives-ouvertes.fr/hal-01300711
Illuminating search spaces by mapping elites, 2015. ,
Sferes v2: Evolvin'in the multi-core world, Proc. of CEC, 2010. ,
URL : https://hal.archives-ouvertes.fr/hal-00687633
Intrinsic motivation systems for autonomous mental development, IEEE Trans. on Evolutionary Computation, vol.11, issue.2, pp.265-286, 2007. ,
The playground experiment: Task-independent development of a curious robot, Proc. of the AAAI Spring Symposium on Developmental Robotics, pp.42-47, 2005. ,
Curiosity-driven exploration by self-supervised prediction, Proc. of ICML, 2017. ,
Gaussian processes for machine learning, 2006. ,
, , 2018.
A Possibility for Implementing Curiosity and Boredom in Modelbuilding Neural Controllers, Proc. of SAB, 1990. ,
Trust region policy optimization, Proc. of ICML, 2015. ,
Mastering the game of go without human knowledge, Nature, vol.550, issue.7676, p.354, 2017. ,
Model-based reinforcement learning with parametrized physical models and optimism-driven exploration, Proc. of ICRA, 2016. ,
A restart CMA evolution strategy with increasing population size, Congress on Evolutionary Computation, 2005. ,
Optimization of Gaussian process hyperparameters using Rprop, Proc. of ESANN, 2013. ,
Limbo: A Flexible Highperformance Library for Gaussian Processes modeling and Data-Efficient Optimization, The Journal of Open Source Software, vol.3, issue.26, p.545, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01884299
Self-adaptive genetic algorithms with simulated binary crossover. Secretary of the SFB 531, 1999. ,
DOI : 10.1162/106365601750190406
URL : https://eldorado.tu-dortmund.de/bitstream/2003/5370/1/ci61.pdf
Multi-Objective Optimization Using Evolutionary Algorithms, 2001. ,
A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. on Evolutionary Computation, vol.6, issue.2, pp.182-197, 2002. ,
DART: Dynamic animation and robotics toolkit, The Journal of Open Source Software, 2018. ,
DOI : 10.21105/joss.00500
URL : https://www.theoj.org/joss-papers/joss.00500/10.21105.joss.00500.pdf
Es is more than just a traditional finite-difference approximator, 2017. ,
CMA-ES with restarts for solving CEC 2013 benchmark problems, Congress on Evolutionary Computation, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00823880
Sferes v2: Evolvin'in the multi-core world, Proc. of CEC, 2010. ,
URL : https://hal.archives-ouvertes.fr/hal-00687633
Deep neuroevolution: Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning, 2017. ,
Gaussian processes for machine learning, 2006. ,
Rprop-a fast adaptive learning algorithm, Proc. of ISCIS VII, 1992. ,
Evolution strategies as a scalable alternative to reinforcement learning, 2017. ,