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Accurately predicting the consequences of agents' actions is a key prerequisite for planning in robotic control.
A markovian decision process
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Deterministic nonperiodic flow
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Digital signal processing
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Optimal control theory: an introduction
Donald E Kirk · 2004
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Gaussian process dynamical models
Jack Wang, Aaron Hertzmann, and David J Fleet · 2006
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PILCO: A Model-Based and Data-Efficient Approach to Policy Search
Marc P. Deisenroth and Carl E. Rasmussen · 2011
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Linear system theory
Frank M Callier and Charles A Desoer · 2012
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Multirotor aerial vehicles: Modeling, estimation, and control of quadrotor
Robert Mahony, Vijay Kumar, and Peter Corke · 2012
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Learning continuous control policies by stochastic value gradients
Nicolas Heess, Greg Wayne, David Silver, Timothy Lillicrap, Yuval Tassa, and Tom Erez · 2015
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Deep learning helicopter dynamics models
Ali Punjani and Pieter Abbeel · 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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Learning quadrotor dynamics using neural network for flight control
Somil Bansal, Anayo K Akametalu, Frank J Jiang, Forrest Laine, and Claire J Tomlin · 2016
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One-shot learning of manipulation skills with online dynamics adaptation and neural network priors
Justin Fu, Sergey Levine, and Pieter Abbeel · 2016
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Goal-driven dynamics learning via bayesian optimization
Somil Bansal, Roberto Calandra, Ted Xiao, Sergey Levine, and Claire J Tomiin · 2017
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Crazyflie 2.0 quadrotor as a platform for research and education in robotics and control engineering
Wojciech Giernacki, Mateusz Skwierczyński, Wojciech Witwicki, Paweł Wroński, and Piotr Kozierski · 2017
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When to trust your model: Model-based policy optimization
Michael Janner, Justin Fu, Marvin Zhang, and Sergey Levine · 2019
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Low-level control of a quadrotor with deep model-based reinforcement learning
Nathan Lambert, Daniel S Drew, Joseph Yaconelli, Sergey Levine, Roberto Calandra, and Kristofer SJ Pister · 2019
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Deep dynamics models for learning dexterous manipulation
Anusha Nagabandi, Kurt Konoglie, Sergey Levine, and Vikash Kumar · 2019
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Benchmarking model-based reinforcement learning
Tingwu Wang, Xuchan Bao, Ignasi Clavera, Jerrick Hoang, Yeming Wen, Eric Langlois, Shunshi Zhang, Guodong Zhang, Pieter Abbeel, and Jimmy Ba · 2019
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Learning to combat compounding-error in model-based reinforcement learning
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Learning multimodal models for robot dynamics online with a mixture of gaussian process experts
Christopher D McKinnon and Angela P Schoellig · 2017
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Information theoretic mpc for model-based reinforcement learning
Grady Williams, Nolan Wagener, Brian Goldfain, Paul Drews, James M Rehg, Byron Boots, and Evangelos A Theodorou · 2017
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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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Model-based reinforcement learning via meta-policy optimization
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Learning-based model predictive control for safe exploration
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Combating the compounding-error problem with a multi-step model
Kavosh Asadi, Dipendra Misra, Seungchan Kim, and Michel L Littman · 2019
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Objective mismatch in model-based reinforcement learning
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Chenjun Xiao, Yifan Wu, Chen Ma, Dale Schuurmans, and Martin Müller · 2019
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Miles Cranmer, Sam Greydanus, Stephan Hoyer, Peter Battaglia, David Spergel, and Shirley Ho · 2020
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Structured mechanical models for robot learning and control
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Knowledge-based learning of nonlinear dynamics and chaos
Tom Z Jiahao, M Ani Hsieh, and Eric Forgoston · 2021
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Model-based micro-data reinforcement learning: What are the crucial model properties and which to choose?
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Characterizing possible failure modes in physics-informed neural networks
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On the importance of hyperparameter optimization for model-based reinforcement learning
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