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Self-supervised learning and data augmentation have significantly reduced the performance gap between state and image-based reinforcement learning agents in continuous control tasks.
Leveraging procedural generation to benchmark reinforcement learning
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Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
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Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
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Human-level control through deep reinforcement learning
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High-dimensional continuous control using generalized advantage estimation, 2015
John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel · 2015
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Improved regularization of convolutional neural networks with cutout
Terrance Devries and Graham W. Taylor · 2017
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Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
S. Gu, E. Holly, T. Lillicrap, and S. Levine · 2017
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Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents, 2017
Marlos C. Machado, Marc G. Bellemare, Erik Talvitie, Joel Veness, Matthew Hausknecht, and Michael Bowling · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world, 2017
Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel · 2017
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Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer, 2017
Sergey Zagoruyko and Nikos Komodakis · 2017
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V. Le · 2017
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Addressing function approximation error in actor-critic methods, 2018
Scott Fujimoto, Herke van Hoof, and David Meger · 2018
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Illuminating generalization in deep reinforcement learning through procedural level generation, 2018
Niels Justesen, Ruben Rodriguez Torrado, Philip Bontrager, Ahmed Khalifa, Julian Togelius, and Sebastian Risi · 2018
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Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation, 2018
Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, and Sergey Levine · 2018
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Gotta learn fast: A new benchmark for generalization in rl, 2018
Alex Nichol, Vicki Pfau, Christopher Hesse, Oleg Klimov, and John Schulman · 2018
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Efficient neural architecture search via parameter sharing
Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, and Jeff Dean · 2018
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Solving rubik’s cube with a robot hand, 2019
OpenAI, Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, Jonas Schneider, Nikolas Tezak, Jerry Tworek, Peter Welinder, Lilian Weng, Qiming Yuan, Wojciech Zaremba, and Lei Zhang · 2019
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Assessing generalization in deep reinforcement learning, 2019
Charles Packer, Katelyn Gao, Jernej Kos, Philipp Krähenbühl, Vladlen Koltun, and Dawn Song · 2019
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Observational overfitting in reinforcement learning, 2019
Xingyou Song, Yiding Jiang, Stephen Tu, Yilun Du, and Behnam Neyshabur · 2019
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Unsupervised data augmentation
Qizhe Xie, Zihang Dai, E. Hovy, Minh-Thang Luong, and Quoc V. Le · 2019
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Improving sample efficiency in model-free reinforcement learning from images, 2019
Denis Yarats, Amy Zhang, Ilya Kostrikov, Brandon Amos, Joelle Pineau, and Rob Fergus · 2019
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Multi-goal reinforcement learning: Challenging robotics environments and request for research, 2018
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Data augmentation using random image cropping and patching for deep cnns
Ryo Takahashi, Takashi Matsubara, and Kuniaki Uehara · 2018
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Deepmind control suite, 2018
Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, Timothy Lillicrap, and Martin Riedmiller · 2018
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Transfer learning for related reinforcement learning tasks via image-to-image translation, 2019
Shani Gamrian and Yoav Goldberg · 2019
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Rlbench: The robot learning benchmark & learning environment, 2019
Stephen James, Zicong Ma, David Rovick Arrojo, and Andrew J. Davison · 2019
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Obstacle tower: A generalization challenge in vision, control, and planning, 2019
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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Network randomization: A simple technique for generalization in deep reinforcement learning, 2019
Kimin Lee, Kibok Lee, Jinwoo Shin, and Honglak Lee · 2019
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Investigating generalisation in continuous deep reinforcement learning, 2019
Chenyang Zhao, Olivier Sigaud, Freek Stulp, and Timothy M. Hospedales · 2019
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Image augmentation is all you need: Regularizing deep reinforcement learning from pixels, 2020
Ilya Kostrikov, Denis Yarats, and Rob Fergus · 2020
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