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Imitation Learning (IL) is an appealing approach to learn desirable autonomous behavior.
Model-based reinforcement learning with an approximate, learned model
Leonid Kuvayev and Richard S. Sutton · 1996
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Planning algorithms
Steven M LaValle · 2006
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Linearly-solvable Markov decision problems
Emanuel Todorov · 2007
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PILCO: A model-based and data-efficient approach to policy search
Marc Deisenroth and Carl E Rasmussen · 2011
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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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Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
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Learning to act by predicting the future
Alexey Dosovitskiy and Vladlen Koltun · 2016
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A survey of motion planning and control techniques for self-driving urban vehicles
Brian Paden, Michal Čáp, Sze Zheng Yong, Dmitry Yershov, and Emilio Frazzoli · 2016
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Value iteration networks
Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, and Pieter Abbeel · 2016
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CARLA: An open urban driving simulator
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun · 2017
Cited alongside, same era.
DropoutDAgger: A Bayesian approach to safe imitation learning
Kunal Menda, Katherine Driggs-Campbell, and Mykel J Kochenderfer · 2017
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Parallel WaveNet: Fast high-fidelity speech synthesis
Aaron van den Oord, Yazhe Li, Igor Babuschkin, Karen Simonyan, Oriol Vinyals, Koray Kavukcuoglu, George van den Driessche, Edward Lockhart, Luis C Cobo, Florian Stimberg, et al · 2017
Cited alongside, same era.
Query-efficient imitation learning for end-to-end simulated driving
Jiakai Zhang and Kyunghyun Cho · 2017
Cited alongside, same era.
Differentiable MPC for end-to-end planning and control
Brandon Amos, Ivan Dario Jimenez Rodriguez, Jacob Sacks, Byron Boots, and Zico Kolter · 2018
CIRL: Controllable imitative reinforcement learning for vision-based self-driving
Xiaodan Liang, Tairui Wang, Luona Yang, and Eric Xing · 2018
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R2P2: A reparameterized pushforward policy for diverse, precise generative path forecasting
Nicholas Rhinehart, Kris M. Kitani, and Paul Vernaza · 2018
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Conditional affordance learning for driving in urban environments
Axel Sauer, Nikolay Savinov, and Andreas Geiger · 2018
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Planning and decision-making for autonomous vehicles
Wilko Schwarting, Javier Alonso-Mora, and Daniela Rus · 2018
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Universal planning networks: Learning generalizable representations for visuomotor control
Aravind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2018
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Cited alongside, same era.
End-to-end driving via conditional imitation learning
Felipe Codevilla, Matthias Miiller, Antonio López, Vladlen Koltun, and Alexey Dosovitskiy · 2018
Cited alongside, same era.
Reinforcement learning and control as probabilistic inference: Tutorial and review
Sergey Levine · 2018
Cited alongside, same era.
Zhihao Li, Toshiyuki Motoyoshi, Kazuma Sasaki, Tetsuya Ogata, and Shigeki Sugano · 2018
Cited alongside, same era.
A fast integrated planning and control framework for autonomous driving via imitation learning
Liting Sun, Cheng Peng, Wei Zhan, and Masayoshi Tomizuka · 2018
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Exploring the limitations of behavior cloning for autonomous driving
Felipe Codevilla, Eder Santana, Antonio M López, and Adrien Gaidon · 2019
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Human motion trajectory prediction: A survey
Andrey Rudenko, Luigi Palmieri, Michael Herman, Kris M Kitani, Dariu M Gavrila, and Kai O Arras · 2019
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