Bidirectional relation between CMA evolution strategies and natural evolution strategies, Proc. of PPSN, pp.154-163, 2010. ,
Optimal filtering. Courier Corporation, 2012. ,
Hindsight experience replay, Proc. of NIPS, 2017. ,
Underwater robotics. Springer handbook of robotics, vol.15, pp.987-1008, 2008. ,
A survey of robot learning from demonstration, Robotics and Autonomous Systems, vol.57, issue.5, pp.469-483, 2009. ,
Deep reinforcement learning: A brief survey, IEEE Signal Processing Magazine, vol.34, issue.6, pp.26-38, 2017. ,
Locally weighted learning, Lazy learning, pp.11-73, 1997. ,
A comparison of direct and model-based reinforcement learning, IEEE International Conference on, vol.4, pp.3557-3564, 1997. ,
Finite-time analysis of the multiarmed bandit problem, Machine learning, vol.47, issue.2-3, pp.235-256, 2002. ,
A restart CMA evolution strategy with increasing population size, Congress on Evolutionary Computation, 2005. ,
Infinite-horizon policy-gradient estimation, Journal of Artificial Intelligence Research, vol.15, pp.319-350, 2001. ,
Unifying count-based exploration and intrinsic motivation, Proc. of NIPS, 2016. ,
Robotics in remote and hostile environments, Science, vol.318, issue.5853, pp.1098-1102, 2007. ,
A markovian decision process, Journal of mathematics and mechanics, pp.679-684, 1957. ,
Incremental natural actor-critic algorithms, Advances in neural information processing systems, pp.105-112, 2008. ,
Robot programming by demonstration, Springer handbook of robotics, pp.1371-1394, 2008. ,
Survey: Robot programming by demonstration. Handbook of robotics, p.59, 2008. ,
Pattern recognition and machine learning, 2006. ,
Optimization of Gaussian process hyperparameters using Rprop, Proc. of ESANN, 2013. ,
Resilient machines through continuous self-modeling, Science, vol.314, issue.5802, pp.1118-1121, 2006. ,
The cross-entropy method for optimization, Handbook of statistics, vol.31, pp.35-59, 2013. ,
Random forests. Machine learning, vol.45, pp.5-32, 2001. ,
A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning, 2010. ,
Elephants don't play chess, Robotics and autonomous systems, vol.6, issue.1-2, pp.3-15, 1990. ,
Intelligence without reason, Proceedings of the 12th international joint conference on Artificial intelligence, vol.1, pp.569-595, 1991. ,
Intelligence without representation. Artificial intelligence, vol.47, pp.139-159, 1991. ,
The role of learning in autonomous robots, Proceedings of the fourth annual workshop on Computational learning theory, pp.5-10, 2014. ,
On learning, representing, and generalizing a task in a humanoid robot, IEEE Transactions on Systems, Man, and Cybernetics, vol.37, issue.2, pp.286-298, 2007. ,
Model predictive control, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00256633
Developmental robotics: From babies to robots, 2015. ,
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
Black-Box Data-efficient Policy Search for Robotics, Proc. of IROS, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01576683
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
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
Deep reinforcement learning in a handful of trials using probabilistic dynamics models, Advances in Neural Information Processing Systems, pp.4754-4765, 2018. ,
Model-based reinforcement learning via meta-policy optimization, Conference on Robot Learning, pp.617-629, 2018. ,
GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms, Proc. of ICML, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01840576
Natural language processing (almost) from scratch, Journal of machine learning research, vol.12, pp.2493-2537, 2011. ,
Bullet physics library. Open source: bulletphysics. org, vol.15, p.5, 2013. ,
Limbo: A Flexible High-performance 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
Robots that can adapt like animals, Nature, vol.521, issue.7553, pp.503-507, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01158243
Quality and diversity optimization: A unifying modular framework, IEEE Transactions on Evolutionary Computation, vol.22, issue.2, pp.245-259, 2017. ,
Hierarchical behavioral repertoires with unsupervised descriptors, Proceedings of the Genetic and Evolutionary Computation Conference, pp.69-76, 2018. ,
Quality and diversity optimization: A unifying modular framework, IEEE Trans. on Evolutionary Computation, vol.22, issue.2, pp.245-259, 2018. ,
Evolving a behavioral repertoire for a walking robot, Evolutionary Computation, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01095543
Efficient reinforcement learning for robots using informative simulated priors, Proc. of ICRA, 2015. ,
Using expectation-maximization for reinforcement learning, Neural Computation, vol.9, issue.2, pp.271-278, 1997. ,
Multi-objective optimization using evolutionary algorithms, vol.16, 2001. ,
Self-adaptive genetic algorithms with simulated binary crossover, 1999. ,
A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. on Evolutionary Computation, vol.6, issue.2, pp.182-197, 2002. ,
Gaussian processes for data-efficient 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. ,
PILCO: A model-based and data-efficient approach to policy search, Proc. of ICML, 2011. ,
Evolutionary robotics: what, why, and where to, Frontiers in Robotics and AI, vol.2, p.4, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01131267
Open-ended learning: a conceptual framework based on representational redescription, Frontiers in neurorobotics, vol.12, p.59, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01889947
Novelty search: a theoretical perspective, Proceedings of the Genetic and Evolutionary Computation Conference, pp.99-106, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02561846
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
Evolutionary robotics: Exploring new horizons, New horizons in evolutionary robotics, pp.3-25, 2011. ,
URL : https://hal.archives-ouvertes.fr/inria-00566896
Evolution of repertoire-based control for robots with complex locomotor systems, IEEE Transactions on Evolutionary Computation, vol.22, issue.2, pp.314-328, 2017. ,
Evolution of repertoire-based control for robots with complex locomotor systems, IEEE Transactions on Evolutionary Computation, vol.22, pp.314-328, 2018. ,
Learning ball acquisition on a physical robot, International Symposium on Robotics and Automation (ISRA), p.6, 2004. ,
Model-agnostic meta-learning for fast adaptation of deep networks, Proceedings of the 34th International Conference on Machine Learning, vol.70, pp.1126-1135, 2017. ,
Reverse curriculum generation for reinforcement learning, Conference on Robot Learning, 2017. ,
Dropout as a bayesian approximation: Representing model uncertainty in deep learning, Proc. of ICML, 2015. ,
Concrete dropout, Advances in Neural Information Processing Systems, pp.3581-3590, 2017. ,
Improving PILCO with bayesian neural network dynamics models, Data-Efficient Machine Learning workshop, 2016. ,
Application of evolved locomotion controllers to a hexapod robot, Robotics and Autonomous Systems, vol.19, issue.1, pp.95-103, 1996. ,
Building an affordances map with interactive perception, 2019. ,
A comparative analysis of selection schemes used in genetic algorithms, Foundations of genetic algorithms, vol.1, pp.69-93, 1991. ,
Information-seeking, curiosity, and attention: computational and neural mechanisms, Trends in Cognitive Sciences, vol.17, issue.11, pp.585-593, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00913646
GPy: A gaussian process framework in python, 2012. ,
Straight-leg walking through underconstrained whole-body control, 2018 IEEE International Conference on Robotics and Automation (ICRA), pp.1-5, 2018. ,
Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates, 2017 IEEE international conference on robotics and automation (ICRA), pp.3389-3396, 2017. ,
On calibration of modern neural networks, Proc. of ICML, 2017. ,
Recurrent world models facilitate policy evolution, Advances in Neural Information Processing Systems, pp.2450-2462, 2018. ,
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor, International Conference on Machine Learning, pp.1861-1870, 2018. ,
, Noise contrastive priors for functional uncertainty, 2018.
The CMA Evolution Strategy: A Comparing Review, 2006. ,
Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed, Proc. of GECCO, pp.2389-2396, 2009. ,
URL : https://hal.archives-ouvertes.fr/inria-00382093
Benchmarking a BI-population CMA-ES on the BBOB-2009 noisy testbed, Proc. of GECCO, pp.2397-2402, 2009. ,
URL : https://hal.archives-ouvertes.fr/inria-00382101
A method for handling uncertainty in evolutionary optimization with an application to feedback control of combustion, IEEE Trans. on Evolutionary Computation, vol.13, issue.1, pp.180-197, 2009. ,
URL : https://hal.archives-ouvertes.fr/inria-00276216
Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaptation, Proc. of IEEE international conference on evolutionary computation, pp.312-317, 1996. ,
, Emergence of locomotion behaviours in rich environments, 2017.
Fast sparse gaussian process methods: The informative vector machine, Advances in neural information processing systems, pp.625-632, 2003. ,
Synthesizing neural network controllers with probabilistic model-based reinforcement learning, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp.2538-2544, 2018. ,
Model Identification, pp.113-138, 2016. ,
Vime: Variational information maximizing exploration, Proc. of NIPS, 2016. ,
Grasping novel objects with a dexterous robotic hand through neuroevolution, 2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA), pp.1-8, 2014. ,
Learning attractor landscapes for learning motor primitives, Advances in neural information processing systems, pp.1547-1554, 2003. ,
Evolutionary optimization in uncertain environments-a survey, IEEE Trans. on Evolutionary Computation, vol.9, issue.3, pp.303-317, 2005. ,
Lipschitzian optimization without the lipschitz constant, Journal of optimization Theory and Applications, vol.79, issue.1, pp.157-181, 1993. ,
Learning state representations with robotic priors, Autonomous Robots, vol.39, issue.3, pp.407-428, 2015. ,
Unscented filtering and nonlinear estimation, Proceedings of the IEEE, vol.92, issue.3, pp.401-422, 2004. ,
Reinforcement learning: A survey, Journal of artificial intelligence research, vol.4, pp.237-285, 1996. ,
A natural policy gradient, Advances in neural information processing systems, pp.1531-1538, 2002. ,
Beyond modularity: A developmental perspective on cognitive science, European journal of disorders of communication, vol.29, issue.1, pp.95-105, 1994. ,
Fast Online Adaptation in Robotics through Meta-Learning Embeddings of Simulated Priors, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020. ,
URL : https://hal.archives-ouvertes.fr/hal-02909452
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
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
A matching pursuit approach to sparse gaussian process regression, Advances in neural information processing systems, pp.643-650, 2006. ,
Curse of dimensionality. Encyclopedia of machine learning, pp.257-258, 2010. ,
Reinforcement learning in robotics: A survey, International Journal of Robotics Research, vol.32, issue.11, pp.1238-1274, 2013. ,
Policy search for motor primitives in robotics, Advances in neural information processing systems, pp.849-856, 2009. ,
Policy gradient reinforcement learning for fast quadrupedal locomotion, Proc. of ICRA, vol.3, pp.2619-2624, 2004. ,
Fast damage recovery in robotics with the t-resilience algorithm, The International Journal of Robotics Research, vol.32, issue.14, pp.1700-1723, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00932862
Online discovery of locomotion modes for wheel-legged hybrid robots: A transferability-based approach, Field Robotics, pp.70-77, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-00633930
The transferability approach: Crossing the reality gap in evolutionary robotics, IEEE Transactions on Evolutionary Computation, vol.17, issue.1, pp.122-145, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-00687617
Neuroevolutionary reinforcement learning for generalized helicopter control, Proceedings of the 11th Annual conference on Genetic and evolutionary computation, pp.145-152, 2009. ,
The darpa robotics challenge finals: Results and perspectives, Journal of Field Robotics, vol.34, issue.2, pp.229-240, 2017. ,
Optimal control with learned local models: Application to dexterous manipulation, 2016 IEEE International Conference on Robotics and Automation (ICRA), pp.378-383, 2016. ,
Model-based contextual policy search for data-efficient generalization of robot skills, Artificial Intelligence, vol.247, pp.415-439, 2017. ,
Modelensemble trust-region policy optimization, Proc. of ICLR, 2018. ,
Simple and scalable predictive uncertainty estimation using deep ensembles, Advances in Neural Information Processing Systems, pp.6402-6413, 2017. ,
DART: Dynamic animation and robotics toolkit, The Journal of Open Source Software, 2018. ,
Es is more than just a traditional finite-difference approximator, 2017. ,
Abandoning objectives: Evolution through the search for novelty alone, Evolutionary computation, vol.19, issue.2, pp.189-223, 2011. ,
Evolving a diversity of virtual creatures through novelty search and local competition, Proceedings of the 13th annual conference on Genetic and evolutionary computation, pp.211-218, 2011. ,
State representation learning for control: An overview, Neural Networks, vol.108, pp.379-392, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01858558
Learning neural network policies with guided policy search under unknown dynamics, Proc. of NIPS, pp.1071-1079, 2014. ,
Guided policy search, Proc. of ICML, 2013. ,
Learning hand-eye coordination for robotic grasping with deep learning and largescale data collection, The International Journal of Robotics Research, vol.37, issue.4-5, pp.421-436, 2018. ,
Continuous control with deep reinforcement learning, Proc. of ICLR, 2016. ,
When gaussian process meets big data: A review of scalable gps, IEEE Transactions on Neural Networks and Learning Systems, 2020. ,
Automatic gait optimization with gaussian process regression, IJCAI, vol.7, pp.944-949, 2007. ,
Exploration in model-based reinforcement learning by empirically estimating learning progress, Proc. of NIPS, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-00755248
CMA-ES with restarts for solving CEC 2013 benchmark problems, Congress on Evolutionary Computation, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00823880
Developmental robotics: a survey, Connection science, vol.15, issue.4, pp.151-190, 2003. ,
, Modern Robotics: Mechanics, Planning, and Control, 2017.
A practical bayesian framework for backpropagation networks, Neural computation, vol.4, issue.3, pp.448-472, 1992. ,
Simple random search of static linear policies is competitive for reinforcement learning, Advances in Neural Information Processing Systems, pp.1800-1809, 2018. ,
The cross entropy method for fast policy search, Proc. of ICML, pp.512-519, 2003. ,
, Asynchronous methods for deep reinforcement learning, 2016.
Human-level control through deep reinforcement learning, Nature, vol.518, issue.7540, pp.529-533, 2015. ,
Playing atari with deep reinforcement learning, 2013. ,
Human-level control through deep reinforcement learning, Nature, vol.518, issue.7540, p.529, 2015. ,
Novelty-based Multiobjectivization, New Horizons in Evolutionary Robotics, pp.139-154, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-01300711
Micro-data learning: The other end of the spectrum, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01374786
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
Crossing the reality gap: a short introduction to the transferability approach, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-01300706
A mathematical introduction to robotic manipulation, 1994. ,
Learning to adapt in dynamic, real-world environments through meta-reinforcement learning, Proc. of ICLR, 2019. ,
Neural network dynamics for model-based deep reinforcement learning with model-free finetuning, 2018 IEEE International Conference on Robotics and Automation (ICRA), pp.7559-7566, 2018. ,
Emergency response to the nuclear accident at the Fukushima Daiichi Nuclear Power Plants using mobile rescue robots, Journal of Field Robotics, vol.30, issue.1, pp.44-63, 2013. ,
Bayesian learning for neural networks, vol.118, 2012. ,
PEGASUS: a policy search method for large MDPs and POMDPs, Proc. of Uncertainty in Artificial Intelligence, pp.406-415, 2000. ,
Autonomous helicopter flight via reinforcement learning, Proceedings of the 16th International Conference on Neural Information Processing Systems, NIPS'03, pp.799-806, 2003. ,
Model learning for robot control: a survey, Cognitive Processing, vol.12, issue.4, pp.319-340, 2011. ,
On first-order meta-learning algorithms, 2018. ,
Evolutionary robotics: The biology, intelligence, and technology of self-organizing machines, 2000. ,
Optimization of humanoid walking controller: Crossing the reality gap, 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids), pp.106-111, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-01300704
An algorithmic perspective on imitation learning, Foundations and Trends R in Robotics, vol.7, issue.1-2, pp.1-179, 2018. ,
Risk versus uncertainty in deep learning: Bayes, bootstrap and the dangers of dropout, NIPS Workshop on Bayesian Deep Learning, 2016. ,
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. ,
Whole-body multi-contact motion in humans and humanoids: Advances of the codyco european project, Robotics and Autonomous Systems, vol.90, pp.97-117, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01399360
Sample efficient path integral control under uncertainty, Advances in Neural Information Processing Systems, pp.2314-2322, 2015. ,
Unsupervised learning and exploration of reachable outcome space, 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020. ,
URL : https://hal.archives-ouvertes.fr/hal-02951255
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
Patchwork kriging for large-scale gaussian process regression, 2017. ,
Bayesian optimization with automatic prior selection for data-efficient direct policy search, Proc. of ICRA, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01768279
Learning task-parameterized dynamic movement primitives using mixture of GMMs, Intelligent Service Robotics, vol.11, issue.1, pp.61-78, 2018. ,
Relative entropy policy search, Proc. of AAAI, 2010. ,
Reinforcement learning by reward-weighted regression for operational space control, Proceedings of the 24th international conference on Machine learning, pp.745-750, 2007. ,
Natural actor-critic, Neurocomputing, vol.71, issue.7-9, pp.1180-1190, 2008. ,
Reinforcement learning of motor skills with policy gradients, Neural Networks, vol.21, issue.4, pp.682-697, 2008. ,
Reinforcement learning for humanoid robotics, Proceedings of the third IEEE-RAS international conference on humanoid robots, pp.1-20, 2003. ,
, Deep neuroevolution: Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning, 2017.
How the body shapes the way we think: a new view of intelligence, 2006. ,
Survey of model-based reinforcement learning: Applications on robotics, Journal of Intelligent & Robotic Systems, pp.1-21, 2017. ,
Quality diversity: A new frontier for evolutionary computation, Frontiers in Robotics and AI, vol.3, p.40, 2016. ,
A unifying view of sparse approximate Gaussian process regression, JMLR, vol.6, pp.1939-1959, 2005. ,
Using simulation to improve sample-efficiency of bayesian optimization for bipedal robots, Journal of machine learning research, vol.20, issue.49, pp.1-24, 2019. ,
A survey of numerical methods for optimal control, Advances in the Astronautical Sciences, vol.135, issue.1, pp.497-528, 2009. ,
Gaussian processes for machine learning, 2006. ,
Rprop-a fast adaptive learning algorithm, Proc. of ISCIS VII, 1992. ,
On-line Q-learning using connectionist systems, 1994. ,
, Evolution strategies as a scalable alternative to reinforcement learning, 2017.
Intrinsically motivated open-ended learning in autonomous robots, Frontiers in Neurorobotics, vol.13, p.115, 2020. ,
Data-efficient control policy search using residual dynamics learning, Proc. of IROS, 2017. ,
Dynamics systems vs. optimal control-a unifying view, Progress in brain research, vol.165, pp.425-445, 2007. ,
, Metalearning. Scholarpedia, vol.5, issue.6, p.4650, 2010.
Trust region policy optimization, Proc. of ICML, 2015. ,
, Proximal policy optimization algorithms, 2017.
Bayesian gaussian process models: Pac-bayesian generalisation error bounds and sparse approximations, 2003. ,
Policy gradients with parameter-based exploration for control, Proc. of Artificial Neural Networks, pp.387-396, 2008. ,
Parameter-exploring policy gradients, Neural Networks, vol.23, issue.4, pp.551-559, 2010. ,
Faster and smoother walking of humanoid hrp-2 with passive toe joints, IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.4909-4914, 2006. ,
Taking the human out of the loop: A review of bayesian optimization. Proceedings of the IEEE, vol.104, pp.148-175, 2015. ,
, Dynamicsaware unsupervised discovery of skills, 2019.
Mastering the game of go with deep neural networks and tree search, Nature, vol.529, issue.7587, pp.484-489, 2016. ,
Mastering the game of go with deep neural networks and tree search, Nature, vol.529, issue.7587, pp.484-489, 2016. ,
Mastering chess and shogi by self-play with a general reinforcement learning algorithm, 2017. ,
Deterministic policy gradient algorithms, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-00938992
Sparse gaussian processes using pseudoinputs, Proc. of NIPS, 2005. ,
Trial-anderror learning of repulsors for humanoid qp-based whole-body control, 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), pp.468-475, 2017. ,
Designing neural networks through neuroevolution, Nature Machine Intelligence, vol.1, issue.1, pp.24-35, 2019. ,
Evolving neural networks through augmenting topologies, Evolutionary computation, vol.10, issue.2, pp.99-127, 2002. ,
Deep neuroevolution: Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning, 2017. ,
Least-squares conditional density estimation, IEICE Transactions on Information and Systems, vol.93, issue.3, pp.583-594, 2010. ,
Learning to predict by the methods of temporal differences, Machine learning, vol.3, issue.1, pp.9-44, 1988. ,
Generalization in reinforcement learning: Successful examples using sparse coarse coding, Advances in neural information processing systems, pp.1038-1044, 1996. ,
Reinforcement learning: An introduction, 1998. ,
Policy gradient methods for reinforcement learning with function approximation, Advances in neural information processing systems, pp.1057-1063, 2000. ,
, , 2014.
, Model-based policy gradients with parameter-based exploration by leastsquares conditional density estimation, Neural Netw, vol.57, pp.128-140
Using response surfaces and expected improvement to optimize snake robot gait parameters, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.1069-1074, 2011. ,
A generalized path integral control approach to reinforcement learning, JMLR, vol.11, pp.3137-3181, 2010. ,
Lifelong robot learning. Robotics and autonomous systems, vol.15, pp.25-46, 1995. ,
Robotics in hazardous applications, Springer handbook of robotics, pp.1521-1548, 2016. ,
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
Learning model-free robot control by a monte carlo em algorithm, Autonomous Robots, vol.27, issue.2, pp.123-130, 2009. ,
Q-learning, Machine learning, vol.8, issue.3-4, pp.279-292, 1992. ,
Natural evolution strategies, IEEE Congress on Evolutionary Computation, pp.3381-3387, 2008. ,
Aggressive driving with model predictive path integral control, 2016 IEEE International Conference on Robotics and Automation (ICRA), pp.1433-1440, 2016. ,
Simple statistical gradient-following algorithms for connectionist reinforcement learning, Machine learning, vol.8, issue.3-4, pp.229-256, 1992. ,
Using trajectory data to improve bayesian optimization for reinforcement learning, JMLR, vol.15, issue.1, pp.253-282, 2014. ,
Evolving artificial neural networks, Proceedings of the IEEE, vol.87, pp.1423-1447, 1999. ,
Policy transfer with strategy optimization, International Conference on Learning Representations, 2019. ,
Learning fast adaptation with meta strategy optimization, 2019. ,