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Despite recent progress in reinforcement learning (RL), RL algorithms for exploration still remain an active area of research.
Curious model-building control systems
Jürgen Schmidhuber · 1991
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A rational analysis of the selection task as optimal data selection
Mike Oaksford and Nick Chater · 1994
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Causal learning mechanisms in very young children: two-, three-, and four-year-olds infer causal relations from patterns of variation and covariation
Alison Gopnik, David M Sobel, Laura E Schulz, and Clark Glymour · 2001
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A theory of causal learning in children: causal maps and bayes nets
Alison Gopnik, Clark Glymour, David M Sobel, Laura E Schulz, Tamar Kushnir, and David Danks · 2004
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Learning overhypotheses with hierarchical bayesian models
Charles Kemp, Andrew Perfors, and Joshua B. Tenenbaum · 2007
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Conditional probability versus spatial contiguity in causal learning: Preschoolers use new contingency evidence to overcome prior spatial assumptions
Tamar Kushnir and Alison Gopnik · 2007
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Serious fun: preschoolers engage in more exploratory play when evidence is confounded
Laura E. Schulz and Elizabeth Baraff Bonawitz · 2007
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Theory-based causal induction
Thomas L Griffiths and Joshua B Tenenbaum · 2009
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Experience matters: Information acquisition optimizes probability gain
Jonathan D Nelson, Craig RM McKenzie, Garrison W Cottrell, and Terrence J Sejnowski · 2010
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Where science starts: Spontaneous experiments in preschoolers’ exploratory play
Claire Cook, Noah D Goodman, and Laura E Schulz · 2011
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Scientific thinking in young children: Theoretical advances, empirical research, and policy implications
Alison Gopnik · 2012
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Reconstructing constructivism: Causal models, bayesian learning mechanisms, and the theory theory
Alison Gopnik and Henry M Wellman · 2012
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Exploring explanation: explaining inconsistent evidence informs exploratory, hypothesis-testing behavior in young children
Christine H. Legare · 2012
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Learning about causes from people: observational causal learning in 24-month-old infants
Andrew N Meltzoff, Anna Waismeyer, and Alison Gopnik · 2012
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The origins of inquiry: inductive inference and exploration in early childhood
Laura E. Schulz · 2012
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When children are better (or at least more open-minded) learners than adults: Developmental differences in learning the forms of causal relationships
Christopher G Lucas, Sophie Bridgers, Thomas L Griffiths, and Alison Gopnik · 2014
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Unifying count-based exploration and intrinsic motivation
Marc Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, and Remi Munos · 2016
Cited alongside, same era.
Openai gym, 2016
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Cited alongside, same era.
Deep exploration via bootstrapped dqn
Ian Osband, Charles Blundell, Alexander Pritzel, and Benjamin Van Roy · 2016
Cited alongside, same era.
Changes in cognitive flexibility and hypothesis search across human life history from childhood to adolescence to adulthood
Alison Gopnik, Shaun O’Grady, Christopher G Lucas, Thomas L Griffiths, Adrienne Wente, Sophie Bridgers, Rosie Aboody, Hoki Fung, and Ronald E Dahl · 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.
Causal reasoning from meta-reinforcement learning
Ishita Dasgupta, Jane Wang, Silvia Chiappa, Jovana Mitrovic, Pedro Ortega, David Raposo, Edward Hughes, Peter Battaglia, Matthew Botvinick, and Zeb Kurth-Nelson · 2019
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Causal confusion in imitation learning
Pim de Haan, Dinesh Jayaraman, and Sergey Levine · 2019
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Causal induction from visual observations for goal directed tasks
Suraj Nair, Yuke Zhu, Silvio Savarese, and Li Fei-Fei · 2019
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Self-supervised exploration via disagreement
Deepak Pathak, Dhiraj Gandhi, and Abhinav Gupta · 2019
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Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
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Count-based exploration with neural density models
Georg Ostrovski, Marc G Bellemare, Aäron van den Oord, and Rémi Munos · 2017
Cited alongside, same era.
Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A Efros, and Trevor Darrell · 2017
Cited alongside, same era.
# 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
Cited alongside, same era.
Exploration by random network distillation
Yuri Burda, Harrison Edwards, Amos Storkey, and Oleg Klimov · 2018
Cited alongside, same era.
Babyai: A platform to study the sample efficiency of grounded language learning
Maxime Chevalier-Boisvert, Dzmitry Bahdanau, Salem Lahlou, Lucas Willems, Chitwan Saharia, Thien Huu Nguyen, and Yoshua Bengio · 2018
Cited alongside, same era.
Quantifying generalization in reinforcement learning
Karl Cobbe, Oleg Klimov, Chris Hesse, Taehoon Kim, and John Schulman · 2018
Cited alongside, same era.
Gotta learn fast: A new benchmark for generalization in rl
Alex Nichol, Vicki Pfau, Christopher Hesse, Oleg Klimov, and John Schulman · 2018
Cited alongside, same era.
Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, and Sergey Levine · 2019
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Causalworld: A robotic manipulation benchmark for causal structure and transfer learning
Ossama Ahmed, Frederik Träuble, Anirudh Goyal, Alexander Neitz, Yoshua Bengio, Bernhard Schölkopf, Manuel Wüthrich, and Stefan Bauer · 2020
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Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning
Kelsey R. Allen, Kevin A. Smith, and Joshua B. Tenenbaum · 2020
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Rlbench: The robot learning benchmark & learning environment
Stephen James, Zicong Ma, David Rovick Arrojo, and Andrew J Davison · 2020
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Meta-trained agents implement bayes-optimal agents
Vladimir Mikulik, Grégoire Delétang, Tom McGrath, Tim Genewein, Miljan Martic, Shane Legg, and Pedro A Ortega · 2020
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Causally correct partial models for reinforcement learning
Danilo J Rezende, Ivo Danihelka, George Papamakarios, Nan Rosemary Ke, Ray Jiang, Theophane Weber, Karol Gregor, Hamza Merzic, Fabio Viola, Jane Wang, et al · 2020
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A survey of exploration methods in reinforcement learning
Susan Amin, Maziar Gomrokchi, Harsh Satija, Herke van Hoof, and Doina Precup · 2021
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Systematic evaluation of causal discovery in visual model based reinforcement learning
Nan Rosemary Ke, Aniket Rajiv Didolkar, Sarthak Mittal, Anirudh Goyal, Guillaume Lajoie, Stefan Bauer, Danilo Jimenez Rezende, Michael Curtis Mozer, Yoshua Bengio, and Christopher Pal · 2021
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Causalcity: Complex simulations with agency for causal discovery and reasoning
Daniel McDuff, Yale Song, Jiyoung Lee, Vibhav Vineet, Sai Vemprala, Nicholas Gyde, Hadi Salman, Shuang Ma, Kwanghoon Sohn, and Ashish Kapoor · 2021
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Causal curiosity: Rl agents discovering self-supervised experiments for causal representation learning
Sumedh A Sontakke, Arash Mehrjou, Laurent Itti, and Bernhard Schölkopf · 2021
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Alchemy: A structured task distribution for meta-reinforcement learning
Jane X. Wang, Michael King, Nicolas Porcel, Zeb Kurth-Nelson, Tina Zhu, Charlie Deck, Peter Choy, Mary Cassin, Malcolm Reynolds, Francis Song, Gavin Buttimore, David P. Reichert, Neil Rabinowitz, Loic Matthey, Demis Hassabis, Alexander Lerchner, and Matthew Botvinick · 2021
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Acre: Abstract causal reasoning beyond covariation
Chi Zhang, Baoxiong Jia, Mark Edmonds, Song-Chun Zhu, and Yixin Zhu · 2021
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