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Producing agents that can generalize to a wide range of visually different environments is a significant challenge in reinforcement learning.
Generalization in reinforcement learning: Successful examples using sparse coarse coding
Richard S Sutton · 1996
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Policy gradient methods for reinforcement learning with function approximation
Richard S Sutton, David A McAllester, Satinder P Singh, and Yishay Mansour · 2000
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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
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Ensemble-CIO: Full-body dynamic motion planning that transfers to physical humanoids
Igor Mordatch, Kendall Lowrey, and Emanuel Todorov · 2015
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Towards adapting deep visuomotor representations from simulated to real environments
Eric Tzeng, Coline Devin, Judy Hoffman, Chelsea Finn, Xingchao Peng, Sergey Levine, Kate Saenko, and Trevor Darrell · 2015
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Openai gym, 2016
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Learning transferable policies for monocular reactive mav control
Shreyansh Daftry, J Andrew Bagnell, and Martial Hebert · 2016
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Vizdoom: A doom-based ai research platform for visual reinforcement learning
Michał Kempka, Marek Wydmuch, Grzegorz Runc, Jakub Toczek, and Wojciech Jaśkowski · 2016
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Reinforcement learning for pivoting task
Rika Antonova, Silvia Cruciani, Christian Smith, and Danica Kragic · 2017
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Learning invariant feature spaces to transfer skills with reinforcement learning
Abhishek Gupta, Coline Devin, Yuxuan Liu, Pieter Abbeel, and Sergey Levine · 2017
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Epopt: Learning robust neural network policies using model ensembles
Aravind Rajeswaran, Sarvjeet Ghotra, Sergey Levine, and Balaraman Ravindran · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world
Joshua Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel · 2017
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Measuring and characterizing generalization in deep reinforcement learning
Sam Witty, Jun Ki Lee, Emma Tosch, Akanksha Atrey, Michael Littman, and David Jensen · 2018
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Improving the generalization of visual navigation policies using invariance regularization
Michel Aractingi, Christopher Dance, Julien Perez, and Tomi Silander · 2019
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Quantifying generalization in reinforcement learning
Karl Cobbe, Oleg Klimov, Chris Hesse, Taehoon Kim, and John Schulman · 2019
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Generalization through simulation: Integrating simulated and real data into deep reinforcement learning for vision-based autonomous flight
Katie Kang, Suneel Belkhale, Gregory Kahn, Pieter Abbeel, and Sergey Levine · 2019
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A simple randomization technique for generalization in deep reinforcement learning
Kimin Lee, Kibok Lee, Jinwoo Shin, and Honglak Lee · 2019
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Jesse Farebrother, Marlos C Machado, and Michael Bowling · 2018
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Charles Packer, Katelyn Gao, Jernej Kos, Philipp Krähenbühl, Vladlen Koltun, and Dawn Song · 2018
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Sim-to-real transfer of robotic control with dynamics randomization
Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2018
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A dissection of overfitting and generalization in continuous reinforcement learning
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A study on overfitting in deep reinforcement learning
Chiyuan Zhang, Oriol Vinyals, Remi Munos, and Samy Bengio
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Active domain randomization
Bhairav Mehta, Manfred Diaz, Florian Golemo, Christopher J. Pal, and Liam Paull · 2019
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