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We propose Taylor Series Imitation Learning (TaSIL), a simple augmentation to standard behavior cloning losses in the context of continuous control.
Alvinn: An autonomous land vehicle in a neural network
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Explanation-based neural network learning for robot control
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Decision theoretic generalizations of the pac model for neural net and other learning applications
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On contraction analysis for non-linear systems
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Is imitation learning the route to humanoid robots?
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Nonlinear Systems
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Local rademacher complexities
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A fast randomized algorithm for the approximation of matrices
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Smoothness, low noise and fast rates
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
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Efficient backprop
Yann A LeCun, Léon Bottou, Genevieve B Orr, and Klaus-Robert Müller · 2012
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Understanding machine learning: From theory to algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
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Neural learning of vector fields for encoding stable dynamical systems
Andre Lemme, Klaus Neumann, R. Felix Reinhart, and Jochen J. Steil · 2014
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Jonathan Ho and Stefano Ermon · 2016
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Dan Hendrycks and Kevin Gimpel · 2016
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Openai gym, 2016
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Imitation learning: A survey of learning methods
Ahmed Hussein, Mohamed M. Gaber, Eyad Elyan, and Chrisina Jayne · 2017
JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
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High-Dimensional Probability: An Introduction with Applications in Data Science
Roman Vershynin · 2018
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Learning without mixing: Towards a sharp analysis of linear system identification
Max Simchowitz, Horia Mania, Stephen Tu, Michael I. Jordan, and Benjamin Recht · 2018
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High-dimensional statistics: A non-asymptotic viewpoint , volume 48
Martin J. Wainwright · 2019
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Learning stabilizable nonlinear dynamics with contraction-based regularization
Sumeet Singh, Spencer M. Richards, Vikas Sindhwani, Jean-Jacques E. Slotine, and Marco Pavone · 2020
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Dart: Noise injection for robust imitation learning
Michael Laskey, Jonathan Lee, Roy Fox, Anca Dragan, and Ken Goldberg · 2017
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Learning partially contracting dynamical systems from demonstrations
Harish Ravichandar, Iman Salehi, and Ashwin Dani · 2017
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Sobolev training for neural networks
Wojciech M Czarnecki, Simon Osindero, Max Jaderberg, Grzegorz Swirszcz, and Razvan Pascanu · 2017
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An algorithmic perspective on imitation learning
Takayuki Osa, Joni Pajarinen, Gerhard Neumann, J. Andrew Bagnell, Pieter Abbeel, and Jan Peters · 2018
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End-to-end driving via conditional imitation learning
Felipe Codevilla, Matthias Miiller, Antonio López, Vladlen Koltun, and Alexey Dosovitskiy · 2018
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Learning an approximate model predictive controller with guarantees
Michael Hertneck, Johannes Köhler, Sebastian Trimpe, and Frank Allgöwer · 2018
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Nicholas M. Boffi, Stephen Tu, Nikolai Matni, Jean-Jacques E. Slotine, and Vikas Sindhwani · 2020
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Flax: A neural network library and ecosystem for JAX, 2020
Jonathan Heek, Anselm Levskaya, Avital Oliver, Marvin Ritter, Bertrand Rondepierre, Andreas Steiner, and Marc van Zee · 2020
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Optax: composable gradient transformation and optimisation, in jax!, 2020
Matteo Hessel, David Budden, Fabio Viola, Mihaela Rosca, Eren Sezener, and Tom Hennigan · 2020
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Generalization guarantees for imitation learning
Allen Ren, Sushant Veer, and Anirudha Majumdar · 2021
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On the sample complexity of stability constrained imitation learning
Stephen Tu, Alexander Robey, Tingnan Zhang, and Nikolai Matni · 2021
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Knowledge distillation: A survey
Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao · 2021
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Stable-baselines3: Reliable reinforcement learning implementations
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Imitation learning with stability and safety guarantees
He Yin, Peter Seiler, Ming Jin, and Murat Arcak · 2022
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