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Model-Predictive Control (MPC) is a powerful tool for controlling complex, real-world systems that uses a model to make predictions about future behavior.
Constrained model predictive control: Stability and optimality
David Q Mayne, James B Rawlings, Christopher V Rao, and Pierre OM Scokaert · 2000
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Near-optimal reinforcement learning in polynomial time
Michael Kearns and Satinder Singh · 2002
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Exploration in metric state spaces
Sham Kakade, Michael J Kearns, and John Langford · 2003
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Exploration and apprenticeship learning in reinforcement learning
Pieter Abbeel and Andrew Y Ng · 2005
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Autonomous helicopter aerobatics through apprenticeship learning
Pieter Abbeel, Adam Coates, and Andrew Y Ng · 2010
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Tube-based robust nonlinear model predictive control
David Q Mayne, Erric C Kerrigan, EJ Van Wyk, and Paola Falugi · 2011
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Agnostic system identification for model-based reinforcement learning
Stephane Ross and J Andrew Bagnell · 2012
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An integrated system for real-time model predictive control of humanoid robots
Tom Erez, Kendall Lowrey, Yuval Tassa, Vikash Kumar, Svetoslav Kolev, and Emanuel Todorov · 2013
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Value function approximation and model predictive control
Mingyuan Zhong, Mikala Johnson, Yuval Tassa, Tom Erez, and Emanuel Todorov · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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Aggressive driving with model predictive path integral control
Grady Williams, Paul Drews, Brian Goldfain, James M Rehg, and Evangelos A Theodorou · 2016
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Thinking fast and slow with deep learning and tree search
Thomas Anthony, Zheng Tian, and David Barber · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Mastering chess and shogi by self-play with a general reinforcement learning algorithm
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al · 2017
Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning
Anusha Nagabandi, Gregory Kahn, Ronald S Fearing, and Sergey Levine · 2018
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Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations
Aravind Rajeswaran*, Vikash Kumar*, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, and Sergey Levine · 2018
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Truncated horizon policy search: Combining reinforcement learning & imitation learning
Wen Sun, J Andrew Bagnell, and Byron Boots · 2018
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Bayessim: adaptive domain randomization via probabilistic inference for robotics simulators
Fabio Ramos, Rafael Carvalhaes Possas, and Dieter Fox · 2019
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Model-based active exploration
Pranav Shyam, Wojciech Jaśkowski, and Faustino Gomez · 2019
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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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Soft actor-critic algorithms and applications
Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, et al · 2018
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Plan online, learn offline: Efficient learning and exploration via model-based control
Kendall Lowrey, Aravind Rajeswaran, Sham Kakade, Emanuel Todorov, and Igor Mordatch · 2018
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An online learning approach to model predictive control
Nolan Wagener, Ching-An Cheng, Jacob Sacks, and Byron Boots · 2019
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Information theoretic model predictive q-learning
Mohak Bhardwaj, Ankur Handa, Dieter Fox, and Byron Boots · 2020
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Gilwoo Lee, Brian Hou, Sanjiban Choudhury, and Siddhartha S Srinivasa · 2020
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Lyceum: An efficient and scalable ecosystem for robot learning
Colin Summers, Kendall Lowrey, Aravind Rajeswaran, Siddhartha Srinivasa, and Emanuel Todorov · 2020
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