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State representation learning aims at learning compact representations from raw observations in robotics and control applications.
Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cma-es)
Nikolaus Hansen, Sibylle D Müller, and Petros Koumoutsakos · 2003
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Predictive projections
Nathan Sprague · 2009
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Closing the learning-planning loop with predictive state representations
Byron Boots, Sajid M Siddiqi, and Geoffrey J Gordon · 2011
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Autoencoders, unsupervised learning, and deep architectures
Pierre Baldi · 2012
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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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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Auto-Encoding Variational Bayes
D. P Kingma and M. Welling · 2013
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State representation learning in robotics: Using prior knowledge about physical interaction
Rico Jonschkowski and Oliver Brock · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Learning state representations with robotic priors
Rico Jonschkowski and Oliver Brock · 2015
Cited alongside, same era.
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
Cited alongside, same era.
Embed to control: A locally linear latent dynamics model for control from raw images
Manuel Watter, Jost Springenberg, Joschka Boedecker, and Martin Riedmiller · 2015
Cited alongside, same era.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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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Pybullet physics engine
E. Coumans, Y. Bai, and J. Hsu · 2018
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World Models
D. Ha and J. 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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Stable baselines
Ashley Hill, Antonin Raffin, 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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Learning robotic perception through prior knowledge
Rico Jonschkowski · 2018
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State representation learning for control: An overview
Timothée Lesort, Natalia Díaz-Rodríguez, Jean-François Goudou, and David Filliat · 2018
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Rico Jonschkowski, Roland Hafner, Jonathan Scholz, and Martin A. Riedmiller · 2017
Cited alongside, same era.
Unsupervised state representation learning with robotic priors: a robustness benchmark
Timothée Lesort, Mathieu Seurin, Xinrui Li, Natalia Díaz-Rodríguez, and David Filliat · 2017
Cited alongside, same era.
Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, and Trevor Darrell · 2017
Cited alongside, same era.
Time-contrastive networks: Self-supervised learning from multi-view observation
Pierre Sermanet, Corey Lynch, Jasmine Hsu, and Sergey Levine · 2017
Cited alongside, same era.
Loss is its own reward: Self-supervision for reinforcement learning
Evan Shelhamer, Parsa Mahmoudieh, Max Argus, and Trevor Darrell · 2017
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Simple random search provides a competitive approach to reinforcement learning
Horia Mania, Aurelia Guy, and Benjamin Recht · 2018
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Multi-goal reinforcement learning: Challenging robotics environments and request for research
Matthias Plappert, Marcin Andrychowicz, Alex Ray, Bob McGrew, Bowen Baker, Glenn Powell, Jonas Schneider, Josh Tobin, Maciek Chociej, Peter Welinder, Vikash Kumar, and Wojciech Zaremba · 2018
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Decoupling dynamics and reward for transfer learning
Amy Zhang, Harsh Satija, and Joelle Pineau · 2018
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