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We consider the problem of generalization in reinforcement learning where visual aspects of the observations might differ, e.g.
Provably efficient RL with rich observations via latent state decoding
Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, and John Langford · 1901
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Leveraging procedural generation to benchmark reinforcement learning
Karl Cobbe, Christopher Hesse, Jacob Hilton, and John Schulman · 1912
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Domain adaptation via transfer component analysis
Sinno Jialin Pan, Ivor W Tsang, James T Kwok, and Qiang Yang · 2010
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The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
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Domain-adversarial neural networks
Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, and Mario Marchand · 2014
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Unsupervised domain adaptation by backpropagation, 2014
Yaroslav Ganin and Victor Lempitsky · 2014
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Actor-mimic: Deep multitask and transfer reinforcement learning
Emilio Parisotto, Jimmy Lei Ba, and Ruslan Salakhutdinov · 2015
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The kinetics human action video dataset
Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, et al · 2017
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Deep transfer learning with joint adaptation networks
Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I Jordan · 2017
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Curiosity-driven exploration by self-supervised prediction, 2017
Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, and Trevor Darrell · 2017
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Adversarial discriminative domain adaptation, 2017
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
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David Ha and Jürgen Schmidhuber · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Sim-to-real reinforcement learning for deformable object manipulation
Jan Matas, Stephen James, and Andrew J Davison · 2018
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Mutual information maximization for robust plannable representations
Yiming Ding, Ignasi Clavera, and Pieter Abbeel · 2020
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Self-supervised policy adaptation during deployment, 2020
Nicklas Hansen, Yu Sun, Pieter Abbeel, Alexei A. Efros, Lerrel Pinto, and Xiaolong Wang · 2020
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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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Reinforcement learning with augmented data, 2020
Michael Laskin, Kimin Lee, Adam Stooke, Lerrel Pinto, Pieter Abbeel, and Aravind Srinivas · 2020
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Deep reinforcement and infomax learning
Bogdan Mazoure, Rémi Tachet des Combes, Thang Doan, Philip Bachman, and R Devon Hjelm · 2020
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Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, Timothy P. Lillicrap, and Martin A. Riedmiller · 2018
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Combined reinforcement learning via abstract representations
Vincent François-Lavet, Yoshua Bengio, Doina Precup, and Joelle Pineau · 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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Thomas Chaffre, Julien Moras, Adrien Chan-Hon-Tong, and Julien Marzat · 2020
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Quantifying generalization in reinforcement learning
Karl Cobbe, Oleg Klimov, Chris Hesse, Taehoon Kim, and John Schulman
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Natural environment benchmarks for reinforcement learning
Amy Zhang, Yuxin Wu, and Joelle Pineau
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Invariant causal prediction for block mdps, 2020a
Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, and Doina Precup
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Visual transfer for reinforcement learning via wasserstein domain confusion, 2020
Josh Roy and George Konidaris · 2020
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Robust visual domain randomization for reinforcement learning, 2020
Reda Bahi Slaoui, William R. Clements, Jakob N. Foerster, and Sébastien Toth · 2020
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Curl: Contrastive unsupervised representations for reinforcement learning, 2020
Aravind Srinivas, Michael Laskin, and Pieter Abbeel · 2020
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