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Koopman theory asserts that a nonlinear dynamical system can be mapped to a linear system, where the Koopman operator advances observations of the state forward in time.
Hamiltonian systems and transformation in Hilbert space
B. O. Koopman · 1931
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Synthesis and stabilization of complex behaviors through online trajectory optimization
Yuval Tassa, Tom Erez, and Emanuel Todorov · 2012
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Tensorflow: Large-scale machine learning on heterogeneous systems
Martın Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al · 2015
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Improving multi-step prediction of learned time series models
Arun Venkatraman, Martial Hebert, and J. Andrew Bagnell · 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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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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A disentangled recognition and nonlinear dynamics model for unsupervised learning
Marco Fraccaro, Simon Kamronn, Ulrich Paquet, and Ole Winther · 2017
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Data-driven discovery of Koopman eigenfunctions for control
Eurika Kaiser, J Nathan Kutz, and Steven L Brunton · 2017
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Deep variational Bayes filters: Unsupervised learning of state space models from raw data
Maximilian Karl, Maximilian Soelch, Justin Bayer, and Patrick van der Smagt · 2017
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Structured inference networks for nonlinear state space models
Rahul G Krishnan, Uri Shalit, and David Sontag · 2017
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Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operator
Qianxiao Li, Felix Dietrich, Erik M Bollt, and Ioannis G Kevrekidis · 2017
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Learning multimodal transition dynamics for model-based reinforcement learning
Thomas M Moerland, Joost Broekens, and Catholijn M Jonker · 2017
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Linearly-recurrent autoencoder networks for learning dynamics
Samuel E Otto and Clarence W Rowley · 2017
Learning latent dynamics for planning from pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2018
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Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control
Milan Korda and Igor Mezić · 2018
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Deep learning for universal linear embeddings of nonlinear dynamics
Bethany Lusch, J Nathan Kutz, and Steven L Brunton · 2018
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Deep dynamical modeling and control of unsteady fluid flows
Jeremy Morton, Freddie D Witherden, Antony Jameson, and Mykel J Kochenderfer · 2018
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Visual reinforcement learning with imagined goals
Ashvin V Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, and Sergey Levine · 2018
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Learning Koopman invariant subspaces for dynamic mode decomposition
Naoya Takeishi, Yoshinobu Kawahara, and Takehisa Yairi · 2017
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Robust locally-linear controllable embedding
Ershad Banijamali, Rui Shu, Mohammad Ghavamzadeh, Hung Hai Bui, and Ali Ghodsi · 2018
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Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Kurtland Chua, Roberto Calandra, Rowan McAllister, and Sergey Levine · 2018
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Generalizing Koopman theory to allow for inputs and control
Joshua L Proctor, Steven L Brunton, and J Nathan Kutz · 2018
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Deep state space models for time series forecasting
Syama Sundar Rangapuram, Matthias W Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, and Tim Januschowski · 2018
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