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Scaling end-to-end reinforcement learning to control real robots from vision presents a series of challenges, in particular in terms of sample efficiency.
Closing the learning-planning loop with predictive state representations
Byron Boots, Sajid M Siddiqi, and Geoffrey J Gordon · 2011
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Autonomous reinforcement learning on raw visual input data in a real world application
Sascha Lange, Martin Riedmiller, and Arne Voigtlander · 2012
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Predictive state representations: A new theory for modeling dynamical systems
Satinder P. Singh, Michael R. James, and Matthew R. Rudary · 2012
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Autonomous learning of state representations for control: An emerging field aims to autonomously learn state representations for reinforcement learning agents from their real-world sensor observations
Wendelin Böhmer, Jost Tobias Springenberg, Joschka Boedecker, Martin Riedmiller, and Klaus Obermayer · 2015
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Learning state representations with robotic priors
Rico Jonschkowski and Oliver Brock · 2015
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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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From pixels to torques: Policy learning with deep dynamical models
Niklas Wahlström, Thomas B Schön, and Marc Peter Deisenroth · 2015
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Embed to control: A locally linear latent dynamics model for control from raw images
Manuel Watter, Jost Springenberg, Joschka Boedecker, and Martin Riedmiller · 2015
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Dimensionality reduced reinforcement learning for assistive robots
William Curran, Tim Brys, David Aha, Matthew Taylor, and William D Smart · 2016
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Learning state representation for deep actor-critic control
J. Munk, Jens Kober, and Robert Babuska · 2016
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Hindsight experience replay
Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, OpenAI Pieter Abbeel, and Wojciech Zaremba · 2017
Cited alongside, same era.
Openai baselines
Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, and Yuhuai Wu · 2017
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PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured State Representations
Large-scale study of curiosity-driven learning
Yuri Burda, Harri Edwards, Deepak Pathak, Amos Storkey, Trevor Darrell, and Alexei A Efros · 2018
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Pybullet physics engine
E. Coumans, Y. Bai, and J. Hsu · 2018
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Stable baselines
Ashley Hill, Antonin Raffin, René Traoré, Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, and Yuhuai Wu · 2018
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State representation learning for control: An overview
Timothée Lesort, Natalia Díaz-Rodríguez, Jean-François Goudou, and David Filliat · 2018
Later among the works it cites.
S-RL toolbox: Environments, datasets and evaluation metrics for state representation learning
Antonin Raffin, Ashley Hill, René Traoré, Timothée Lesort, Natalia Díaz-Rodríguez, and David Filliat · 2018
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Rico Jonschkowski, Roland Hafner, Jonathan Scholz, and Martin A. Riedmiller · 2017
Cited alongside, same era.
Timothée Lesort, Mathieu Seurin, Xinrui Li, Natalia Díaz-Rodríguez, and David Filliat · 2019
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