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Research in developmental psychology consistently shows that children explore the world thoroughly and efficiently and that this exploration allows them to learn.
Children’s philosophies. a handbook of child psychology
J. Piaget · 1933
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On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
William R Thompson · 1933
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Studies in spatial learning. i. orientation and the short-cut
Ritchie B. F. Kalish D. Tolman, E. C · 1946
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Some aspects of the sequential design of experiments
Herbert Robbins · 1952
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A dynamic allocation index for the discounted multiarmed bandit problem
John C Gittins and David M Jones · 1979
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Curious model-building control systems
Jürgen Schmidhuber · 1991
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Travel broadens the mind
Joseph J Campos, David I Anderson, Marianne A Barbu-Roth, Edward M Hubbard, Matthew J Hertenstein, and David Witherington · 2000
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Maximizing learning progress: an internal reward system for development
Frédéric Kaplan and Pierre-Yves Oudeyer · 2004
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Preschoolers learn causal structure from conditional interventions
L. E. Schulz, A. Gopnik, and C. Glymour · 2007
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Serious fun: Preschoolers play more when evidence is confounded
L.E. Schulz and E. B. Bonawitz · 2007
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Where science starts: Spontaneous experiments in preschoolers’ exploratory play
C. Cook, N. D. Goodman, and L. E. Schulz · 2011
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Children balance theories and evidence in exploration, explanation, and learning
E. B. Bonawitz, T. Van Schijndel, D. Friel, and L. E. Schulz · 2012
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Exploring explanation: Explaining inconsistent evidence informs exploratory, hypothesis‐testing behavior in young children
C. H. Legare · 2012
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The origins of inquiry: Inductive inference and exploration in early childhood
L.E. Schulz · 2012
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Active learning of inverse models with intrinsically motivated goal exploration in robots
Adrien Baranes and Pierre-Yves Oudeyer · 2013
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The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
Cited alongside, same era.
When children are better (or at least more open-minded) learners than adults: Developmental differences in learning the forms of causal relationships
C. G. Lucas, S. Bridgers, T. L. Griffiths, and A. Gopnik · 2014
Cited alongside, same era.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Cited alongside, same era.
Observing the unexpected enhances infants’ learning and exploration
A. E. Stahl and L. Feigenson · 2015
Cited alongside, same era.
Charles Beattie, Joel Z Leibo, Denis Teplyashin, Tom Ward, Marcus Wainwright, Heinrich Küttler, Andrew Lefrancq, Simon Green, Víctor Valdés, Amir Sadik, et al · 2016
# exploration: A study of count-based exploration for deep reinforcement learning
Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, OpenAI Xi Chen, Yan Duan, John Schulman, Filip DeTurck, and Pieter Abbeel · 2017
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Toddler-inspired visual object learning
Sven Bambach, David Crandall, Linda Smith, and Chen Yu · 2018
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Exploration by random network distillation
Yuri Burda, Harrison Edwards, Amos Storkey, and Oleg Klimov · 2018
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Investigating human priors for playing video games
Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Thomas L Griffiths, and Alexei A Efros · 2018
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An introduction to deep reinforcement learning
Vincent François-Lavet, Peter Henderson, Riashat Islam, Marc G. Bellemare, and Joelle Pineau · 2018
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Cited alongside, same era.
Deep exploration via bootstrapped dqn
Ian Osband, Charles Blundell, Alexander Pritzel, and Benjamin Van Roy · 2016
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Cited alongside, same era.
Ex2: Exploration with exemplar models for deep reinforcement learning
Justin Fu, John Co-Reyes, and Sergey Levine · 2017
Cited alongside, same era.
Changes in cognitive flexibility and hypothesis search across human life history from childhood to adolescence to adulthood
O’Grady S. Lucas C. G. Griffiths T. L. Wente A. Bridgers S. Dahl R. E. Gopnik, A · 2017
Cited alongside, same era.
Neuroscience-inspired artificial intelligence
Demis Hassabis, Dharshan Kumaran, Christopher Summerfield, and Matthew Botvinick · 2017
Cited alongside, same era.
Count-based exploration in feature space for reinforcement learning
Jarryd Martin, Suraj Narayanan Sasikumar, Tom Everitt, and Marcus Hutter · 2017
Cited alongside, same era.
Count-based exploration with neural density models
Georg Ostrovski, Marc G Bellemare, Aäron van den Oord, and Rémi Munos · 2017
Cited alongside, same era.
Recurrent experience replay in distributed reinforcement learning
Steven Kapturowski, Georg Ostrovski, John Quan, Remi Munos, and Will Dabney · 2018
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Count-based exploration with the successor representation
Marlos C Machado, Marc G Bellemare, and Michael Bowling · 2018
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Emergent systematic generalization in a situated agent
Felix Hill, Andrew Lampinen, Rosalia Schneider, Stephen Clark, Matthew Botvinick, James L McClelland, and Adam Santoro · 2019
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Learning from approach-avoid decisions: Children explore and learn more than adults
Emily Liquin and Alison Gopnik · 2019
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Reinforcement learning across development: What insights can we draw from a decade of research?
Kate Nussenbaum and Catherine A. Hartley · 2019
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Self-supervised exploration via disagreement
Deepak Pathak, Dhiraj Gandhi, and Abhinav Gupta · 2019
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Zpd teaching strategies for deep reinforcement learning from demonstrations
Daniel Seita, David Chan, Roshan Rao, Chen Tang, Mandi Zhao, and John Canny · 2019
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It’s about the treasure, not the hunt. children are more explorative on an explore-exploit tasks than adults are
E. Sumner, M. Steyvers, and BW. Sarnecka · 2019
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Design drives discovery in causal learning
Caren M. Walker, Alexandra Rett, and Elizabeth Bonawitz · 2020
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