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Building sample-efficient agents that generalize out-of-distribution (OOD) in real-world settings remains a fundamental unsolved problem on the path towards achieving higher-level cognition.
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Deterministic policy gradient algorithms
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Deep convolutional inverse graphics network
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Stable reinforcement learning with autoencoders for tactile and visual data
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Successor features for transfer in reinforcement learning
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Generalizing skills with semi-supervised reinforcement learning
Chelsea Finn, Tianhe Yu, Justin Fu, Pieter Abbeel, and Sergey Levine · 2017
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Deep predictive policy training using reinforcement learning
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Sim-to-real robot learning from pixels with progressive nets
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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
Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel · 2017
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Understanding disentangling in beta-VAE
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Isolating sources of disentanglement in variational autoencoders
Tian Qi Chen, Xuechen Li, Roger Grosse, and David Duvenaud · 2018
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A framework for the quantitative evaluation of disentangled representations
Cian Eastwood and Christopher KI Williams · 2018
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Neural scene representation and rendering
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Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke Hoof, and David Meger · 2018
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Recurrent world models facilitate policy evolution
David Ha and Jürgen Schmidhuber · 2018
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Stable baselines
Ashley Hill, Antonin Raffin, Maximilian Ernestus, Adam Gleave, Anssi Kanervisto, Rene Traore, Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, and Yuhuai Wu · 2018
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Disentangling by factorising
Hyunjik Kim and Andriy Mnih · 2018
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Variational inference of disentangled latent concepts from unlabeled observations
Abhishek Kumar, Prasanna Sattigeri, and Avinash Balakrishnan · 2018
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Visual reinforcement learning with imagined goals
Learning stable and predictive structures in kinetic systems
Niklas Pfister, Stefan Bauer, and Jonas Peters · 2019
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End-to-end robotic reinforcement learning without reward engineering
Avi Singh, Larry Yang, Kristian Hartikainen, Chelsea Finn, and Sergey Levine · 2019
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Are disentangled representations helpful for abstract visual reasoning?
Sjoerd Van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, and Olivier Bachem · 2019
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Grandmaster level in starcraft ii using multi-agent reinforcement learning
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Learning state abstractions for transfer in continuous control
Kavosh Asadi, David Abel, and Michael L Littman · 2020
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Ashvin V Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, and Sergey Levine · 2018
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Learning deep disentangled embeddings with the f-statistic loss
Karl Ridgeway and Michael C Mozer · 2018
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Effects of degradations on deep neural network architectures
Prasun Roy, Subhankar Ghosh, Saumik Bhattacharya, and Umapada Pal · 2018
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Improving generalization for abstract reasoning tasks using disentangled feature representations
Xander Steenbrugge, Sam Leroux, Tim Verbelen, and Bart Dhoedt · 2018
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A study on overfitting in deep reinforcement learning
Chiyuan Zhang, Oriol Vinyals, Remi Munos, and Samy Bengio · 2018
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Solving rubik’s cube with a robot hand
Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, et al · 2019
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Unsupervised state representation learning in atari
Ankesh Anand, Evan Racah, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre Côté, and R Devon Hjelm · 2019
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Compositionality and generalization in emergent languages
Rahma Chaabouni, Eugene Kharitonov, Diane Bouchacourt, Emmanuel Dupoux, and Marco Baroni · 2020
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On robustness and transferability of convolutional neural networks
Josip Djolonga, Jessica Yung, Michael Tschannen, Rob Romijnders, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Matthias Minderer, Alexander D’Amour, Dan Moldovan, et al · 2020
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In search of lost domain generalization
Ishaan Gulrajani and David Lopez-Paz · 2020
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Weakly-supervised disentanglement without compromises
Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, and Michael Tschannen · 2020
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Curl: Contrastive unsupervised representations for reinforcement learning
Aravind Srinivas, Michael Laskin, and Pieter Abbeel · 2020
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Trifinger: An open-source robot for learning dexterity
Manuel Wüthrich, Felix Widmaier, Felix Grimminger, Joel Akpo, Shruti Joshi, Vaibhav Agrawal, Bilal Hammoud, Majid Khadiv, Miroslav Bogdanovic, Vincent Berenz, et al · 2020
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Learning invariant representations for reinforcement learning without reconstruction
Amy Zhang, Rowan McAllister, Roberto Calandra, Yarin Gal, and Sergey Levine · 2020
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Causalworld: A robotic manipulation benchmark for causal structure and transfer learning
Ossama Ahmed, Frederik Träuble, Anirudh Goyal, Alexander Neitz, Manuel Wüthrich, Yoshua Bengio, Bernhard Schölkopf, and Stefan Bauer · 2021
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Boxhead: A dataset for learning hierarchical representations
Yukun Chen, Andrea Dittadi, Frederik Träuble, Stefan Bauer, and Bernhard Schölkopf · 2021
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Benchmarking structured policies and policy optimization for real-world dexterous object manipulation
Niklas Funk, Charles Schaff, Rishabh Madan, Takuma Yoneda, Julen Urain De Jesus, Joe Watson, Ethan K. Gordon, Felix Widmaier, Stefan Bauer, Siddhartha S. Srinivasa, Tapomayukh Bhattacharjee, Matthew R. Walter, and Jan Peters · 2021
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Systematic evaluation of causal discovery in visual model based reinforcement learning, 2021
Nan Rosemary Ke, Aniket Rajiv Didolkar, Sarthak Mittal, Anirudh Goyal, Guillaume Lajoie, Stefan Bauer, Danilo Jimenez Rezende, Michael Curtis Mozer, Yoshua Bengio, and Christopher Pal · 2021
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Deep reinforcement learning for autonomous driving: A survey
B Ravi Kiran, Ibrahim Sobh, Victor Talpaert, Patrick Mannion, Ahmad A Al Sallab, Senthil Yogamani, and Patrick Pérez · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
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Toward causal representation learning
Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio · 2021
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Decoupling representation learning from reinforcement learning
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On disentangled representations learned from correlated data
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Generalizing to unseen domains: A survey on domain generalization
Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Wenjun Zeng, and Tao Qin · 2021
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Representation matters: Improving perception and exploration for robotics
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