Intrinsically Motivated Learning in Natural and Artificial Systems, vol.9783642323, 2013. ,
DOI : 10.1007/978-3-642-32375-1_1
From Babies to Robots: The Contribution of Developmental Robotics to Developmental Psychology, Child Development Perspectives, 2018. ,
Active learning of inverse models with intrinsically motivated goal exploration in robots, Robotics and Autonomous Systems, vol.61, issue.1, pp.49-73, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00788440
Active learning of parameterized skills, International Conference on Machine Learning, pp.1737-1745, 2014. ,
Intrinsically motivated model learning for developing curious robots, Artificial Intelligence, vol.247, pp.170-186, 2017. ,
DOI : 10.1016/j.artint.2015.05.002
Improving exploration in evolution strategies for deep reinforcement learning via a population of novelty-seeking agents, 2017. ,
GEP-PG: Decoupling exploration and exploitation in deep reinforcement learning, International Conference on Machine Learning (ICML), 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01840576
Robots that can adapt like animals, Nature, vol.521, issue.7553, p.503, 2015. ,
DOI : 10.1038/nature14422
URL : https://hal.archives-ouvertes.fr/hal-01158243
A survey of robot learning from demonstration, Robotics and autonomous systems, vol.57, issue.5, pp.469-483, 2009. ,
Computational theories of curiosity-driven learning, The New Science of Curiosity. NOVA, 2018. ,
DOI : 10.31234/osf.io/3p8f6
URL : http://arxiv.org/pdf/1802.10546
Curiosity and exploration, Science, vol.153, issue.3731, pp.25-33, 1966. ,
Unifying count-based exploration and intrinsic motivation, Advances in Neural Information Processing Systems, pp.1471-1479, 2016. ,
A laplacian framework for option discovery in reinforcement learning, International Conference on Machine Learning, 2017. ,
Intrinsic motivation and reinforcement learning, Intrinsically motivated learning in natural and artificial systems, pp.17-47, 2013. ,
DOI : 10.1007/978-3-642-32375-1_2
Curiosity-driven exploration by selfsupervised prediction, 2017. ,
DOI : 10.1109/cvprw.2017.70
URL : http://arxiv.org/pdf/1705.05363
, Curiosity-driven exploration in deep reinforcement learning via bayesian neural networks, 2016.
# exploration: A study of count-based exploration for deep reinforcement learning, 2016. ,
Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning ,
URL : https://hal.archives-ouvertes.fr/hal-01651233
Goal babbling permits direct learning of inverse kinematics, IEEE Transactions on Autonomous Mental Development, vol.2, issue.3, pp.216-229, 2010. ,
Socially guided intrinsic motivation for robot learning of motor skills, Autonomous Robots, vol.36, issue.3, pp.273-294, 2014. ,
DOI : 10.1007/s10514-013-9339-y
URL : https://hal.archives-ouvertes.fr/hal-00936938
Hindsight Experience Replay, Nips, 2017. ,
Reverse curriculum generation for reinforcement learning, 2017. ,
Powerplay: Training an increasingly general problem solver by continually searching for the simplest still unsolvable problem, Frontiers in psychology, vol.4, p.313, 2013. ,
Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration, ICLR, pp.1-26, 2018. ,
Early Visual Concept Learning with Unsupervised Deep Learning, 2016. ,
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets, Lecture Notes in Computer Science, vol.9006, 2015. ,
Disentangling the independently controllable factors of variation by interacting with the world, pp.1-9, 2017. ,
Intrinsic motivation systems for autonomous mental development, IEEE Transactions on Evolutionary Computation, vol.11, issue.2, pp.265-286, 2007. ,
Representation learning: A review and new perspectives, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.8, pp.1798-1828, 2013. ,
beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework, ICLR, number July, pp.1-13, 2017. ,
Modular active curiosity-driven discovery of tool use, IEEE International Conference on Intelligent Robots and Systems, pp.3965-3972, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01384566
Behavioral Diversity Generation in Autonomous Exploration through Reuse of Past Experience, Frontiers in Robotics and AI, vol.3, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01404329
Zero-Shot Visual Imitation, ICLR, pp.1-12, 2018. ,
Understanding disentangling in ?-VAE, Nips, 2017. ,
(a) Random Parameterization Exploration (b) Random Goal Exploration with Engineered Features Representation (RGE-EFR) (c) Modular Goal Exploration, International Conference on Learning Representations, pp.1-15, 2015. ,
, Examples of achieved outcomes together with the ratio of covered cells in the Arm-2-Balls environment for RPE, MGE-EFR and RGE-EFR exploration algorithms. The number of times the ball was effectively handled is also represented. 20 (a) Random Goal Exploration with an entangled representation (VAE) as a goal space, Figure, vol.12
, Modular Goal Exploration with an entangled representation (VAE) as a goal space
, Random Goal Exploration with a disentangled representation (?VAE) as a goal space
Modular Goal Exploration with a disentangled representation (?VAE) as a goal space ,
, Examples of achieved outcomes together with the ratio of covered cells in the Arm-2-Balls environment for MGE and RGE exploration algorithms using learned goal spaces (VAE and ?VAE). The number of times the ball was effectively handled is also represented, Figure, vol.13