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One of the key challenges in visual Reinforcement Learning (RL) is to learn policies that can generalize to unseen environments.
Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
Ilya Kostrikov, Denis Yarats, and Rob Fergus. 2020 · 2004
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Bootstrap your own latent: A new approach to self-supervised learning
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Learning invariant representations for reinforcement learning without reconstruction
Amy Zhang, Rowan McAllister, Roberto Calandra, Yarin Gal, and Sergey Levine. 2020 · 2006
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Self-supervised policy adaptation during deployment
Nicklas Hansen, Rishabh Jangir, Yu Sun, Guillem Alenyà, Pieter Abbeel, Alexei A Efros, Lerrel Pinto, and Xiaolong Wang. 2020 · 2007
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Human-level control through deep reinforcement learning
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Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel. 2017 · 2017
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Target-driven visual navigation in indoor scenes using deep reinforcement learning
Yuke Zhu, Roozbeh Mottaghi, Eric Kolve, Joseph J Lim, Abhinav Gupta, Li Fei-Fei, and Ali Farhadi. 2017 · 2017
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Lipschitz regularized deep neural networks generalize and are adversarially robust
Chris Finlay, Jeff Calder, Bilal Abbasi, and Adam Oberman. 2018 · 2018
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Scalable deep reinforcement learning for vision-based robotic manipulation
Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, et al. 2018 · 2018
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dm_control: Software and tasks for continuous control
Saran Tunyasuvunakool, Alistair Muldal, Yotam Doron, Siqi Liu, Steven Bohez, Josh Merel, Tom Erez, Timothy Lillicrap, Nicolas Heess, and Yuval Tassa. 2020 · 2020
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Secant: Self-expert cloning for zero-shot generalization of visual policies
Linxi Fan, Guanzhi Wang, De-An Huang, Zhiding Yu, Li Fei-Fei, Yuke Zhu, and Anima Anandkumar. 2021 · 2021
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Stabilizing deep q-learning with convnets and vision transformers under data augmentation
Nicklas Hansen, Hao Su, and Xiaolong Wang. 2021 · 2021
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Generalization in reinforcement learning by soft data augmentation
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Training robust neural networks using lipschitz bounds
Patricia Pauli, Anne Koch, Julian Berberich, Paul Kohler, and Frank Allgower. 2021 · 2021
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Chiyuan Zhang, Oriol Vinyals, Remi Munos, and Samy Bengio. 2018 · 2018
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Quantifying generalization in reinforcement learning
Karl Cobbe, Oleg Klimov, Chris Hesse, Taehoon Kim, and John Schulman. 2019 · 2019
Cited alongside, same era.
A survey on image data augmentation for deep learning
Connor Shorten and Taghi M Khoshgoftaar. 2019 · 2019
Cited alongside, same era.
Reinforcement learning with augmented data
Misha Laskin, Kimin Lee, Adam Stooke, Lerrel Pinto, Pieter Abbeel, and Aravind Srinivas. 2020 · 2020
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Automatic data augmentation for generalization in reinforcement learning
Roberta Raileanu, Maxwell Goldstein, Denis Yarats, Ilya Kostrikov, and Rob Fergus. 2021 · 2021
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Leveraging procedural generation to benchmark reinforcement learning
Karl Cobbe, Chris Hesse, Jacob Hilton, and John Schulman. 2020 · 2056
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