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Infants are experts at playing, with an amazing ability to generate novel structured behaviors in unstructured environments that lack clear extrinsic reward signals.
Perception and the conditioned reflex
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Reinforcement Learning for Robots Using Neural Networks
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People thinking about thinking people: the role of the temporo-parietal junction in “theory of mind”
R. Saxe and N. Kanwisher · 2003
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Intrinsically motivated reinforcement learning
N. Chentanez, A. G. Barto, and S. P. Singh · 2005
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Query by committee made real
R. Gilad-Bachrach, A. Navot, and N. Tishby · 2005
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A. Boeing and T. Bräunl · 2007
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Intrinsic motivation systems for autonomous mental development
P.-Y. Oudeyer, F. Kaplan, and V. V. Hafner · 2007
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The Scientist In The Crib: Minds, Brains, And How Children Learn
A. Gopnik, A. Meltzoff, and P. Kuhl · 2009
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K. B. Hurley, K. A. Kovack-Lesh, and L. M. Oakes · 2010
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J. Schmidhuber · 2010
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S. P. Singh, R. L. Lewis, A. G. Barto, and J. Sorg · 2010
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Active Learning , volume 18
B. Settles · 2011
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The goldilocks effect: Human infants allocate attention to visual sequences that are neither too simple nor too complex
C. Kidd, S. T. Piantadosi, and R. N. Aslin · 2012
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Active learning of inverse models with intrinsically motivated goal exploration in robots
A. Baranes and P.-Y. Oudeyer · 2013
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A convex optimization framework for active learning
E. Elhamifar, G. Sapiro, A. Y. Yang, and S. S. Sastry · 2013
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Information-seeking, curiosity, and attention: computational and neural mechanisms
J. Gottlieb, P.-Y. Oudeyer, M. Lopes, and A. Baranes · 2013
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Infants learn what they want to learn: Responding to infant pointing leads to superior learning
K. Begus, T. Gliga, and V. Southgate · 2014
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Curiosity driven reinforcement learning for motion planning on humanoids
M. Frank, J. Leitner, M. F. Stollenga, A. Förster, and J. Schmidhuber · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Experience and distribution of attention: Pet exposure and infants’ scanning of animal images
K. B. Hurley and L. M. Oakes · 2015
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Surprise-based intrinsic motivation for deep reinforcement learning
J. Achiam and S. Sastry · 2017
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Self-supervised visual planning with temporal skip connections
F. Ebert, C. Finn, A. X. Lee, and S. Levine · 2017
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Deep visual foresight for planning robot motion
C. Finn and S. Levine · 2017
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Automatic goal generation for reinforcement learning agents
D. Held, X. Geng, C. Florensa, and P. Abbeel · 2017
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Weakly supervised semantic segmentation using web-crawled videos
S. Hong, D. Yeo, S. Kwak, H. Lee, and B. Han · 2017
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Video pixel networks
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Learning to poke by poking: Experiential learning of intuitive physics
P. Agrawal, A. V. Nair, P. Abbeel, J. Malik, and S. Levine · 2016
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Infants ask for help when they know they don’t know
L. Goupil, M. Romand-Monnier, and S. Kouider · 2016
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Generative adversarial imitation learning
J. Ho and S. Ermon · 2016
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Vime: Variational information maximizing exploration
R. Houthooft, X. Chen, X. Chen, Y. Duan, J. Schulman, F. D. Turck, and P. Abbeel · 2016
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Reinforcement learning with unsupervised auxiliary tasks
M. Jaderberg, V. Mnih, W. M. Czarnecki, T. Schaul, J. Z. Leibo, D. Silver, and K. Kavukcuoglu · 2016
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Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation
T. D. Kulkarni, K. Narasimhan, A. Saeedi, and J. Tenenbaum · 2016
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Asynchronous methods for deep reinforcement learning
V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Harley, T. P. Lillicrap, D. Silver, and K. Kavukcuoglu · 2016
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N. Kalchbrenner, A. van den Oord, K. Simonyan, I. Danihelka, O. Vinyals, A. Graves, and K. Kavukcuoglu · 2017
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A laplacian framework for option discovery in reinforcement learning
M. C. Machado, M. G. Bellemare, and M. Bowling · 2017
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A self-supervised learning system for object detection using physics simulation and multi-view pose estimation
C. Mitash, K. E. Bekris, and A. Boularias · 2017
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Curiosity-driven exploration by self-supervised prediction
D. Pathak, P. Agrawal, A. A. Efros, and T. Darrell · 2017
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Data-efficient deep reinforcement learning for dexterous manipulation
I. Popov, N. Heess, T. Lillicrap, R. Hafner, G. Barth-Maron, M. Vecerik, T. Lampe, Y. Tassa, T. Erez, and M. Riedmiller · 2017
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A geometric approach to active learning for convolutional neural networks
O. Sener and S. Savarese · 2017
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Curiosity-based learning in infants: a neurocomputational approach
K. E. Twomey and G. Westermann · 2017
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Cost-effective active learning for deep image classification
K. Wang, D. Zhang, Y. Li, R. Zhang, and L. Lin · 2017
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Flexible neural representation for physics prediction
D. Mrowca, C. Zhuang, E. Wang, N. Haber, L. Fei-Fei, J. B. Tenenbaum, and D. L. Yamins · 2018
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Unsupervised representation learning by predicting image rotations
N. K. Spyros Gidaris, Praveer Singh · 2018
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