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MineRL 2019 competition challenged participants to train sample-efficient agents to play Minecraft, by using a dataset of human gameplay and a limit number of steps the environment.
The minerl competition on sample efficient reinforcement learning using human priors
William H Guss, Cayden Codel, Katja Hofmann, Brandon Houghton, Noboru Kuno, Stephanie Milani, Sharada Mohanty, Diego Perez Liebana, Ruslan Salakhutdinov, Nicholay Topin, et al · 1904
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Alvinn: An autonomous land vehicle in a neural network
Dean A Pomerleau · 1989
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Smote: synthetic minority over-sampling technique
Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer · 2002
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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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
Earlier work this paper cites.
End to end learning for self-driving cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al · 2016
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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The malmo platform for artificial intelligence experimentation
Matthew Johnson, Katja Hofmann, Tim Hutton, and David Bignell · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Learning to act by predicting the future
Alexey Dosovitskiy and Vladlen Koltun · 2017
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End-to-end driving via conditional imitation learning
Felipe Codevilla, Matthias Miiller, Antonio López, Vladlen Koltun, and Alexey Dosovitskiy · 2018
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Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures
Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Volodymir Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, et al · 2018
Cited alongside, same era.
Deep reinforcement learning that matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2018
Causal confusion in imitation learning
Pim de Haan, Dinesh Jayaraman, and Sergey Levine · 2019
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Benchmarking batch deep reinforcement learning algorithms
Scott Fujimoto, Edoardo Conti, Mohammad Ghavamzadeh, and Joelle Pineau · 2019
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Obstacle tower: A generalization challenge in vision, control, and planning
Arthur Juliani, Ahmed Khalifa, Vincent-Pierre Berges, Jonathan Harper, Ervin Teng, Hunter Henry, Adam Crespi, Julian Togelius, and Danny Lange · 2019
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Competing in the obstacle tower challenge
Alex Nichol · 2019
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Grandmaster level in starcraft ii using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al · 2019
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Monocular plan view networks for autonomous driving
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Cited alongside, same era.
Deep q-learning from demonstrations
Todd Hester, Matej Vecerik, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Ian Osband, et al · 2018
Cited alongside, same era.
The multi-agent reinforcement learning in malmö (MARLÖ) competition
Diego Perez-Liebana, Katja Hofmann, Sharada Prasanna Mohanty, Noburu Kuno, Andre Kramer, Sam Devlin, Raluca D Gaina, and Daniel Ionita · 2018
Cited alongside, same era.
MineRL: A large-scale dataset of Minecraft demonstrations
William H. Guss, Brandon Houghton, Nicholay Topin, Phillip Wang, Cayden Codel, Manuela Veloso, and Ruslan Salakhutdinov
Cited in the paper.
Dequan Wang, Coline Devin, Qi-Zhi Cai, Philipp Krähenbühl, and Trevor Darrell · 2019
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Fixup initialization: Residual learning without normalization
Hongyi Zhang, Yann N Dauphin, and Tengyu Ma · 2019
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From video game to real robot: The transfer between action spaces
Janne Karttunen, Anssi Kanervisto, Ville Hautamäki, and Ville Kyrki · 2020
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