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We learn an interactive vision-based driving policy from pre-recorded driving logs via a model-based approach.
Alvinn: An autonomous land vehicle in a neural network
Dean A Pomerleau · 1989
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Decision-theoretic planning: Structural assumptions and computational leverage
Craig Boutilier, Thomas Dean, and Steve Hanks · 1999
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The cross entropy method for fast policy search
Shie Mannor, Reuven Y Rubinstein, and Yohai Gat · 2003
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Off-road obstacle avoidance through end-to-end learning
Urs Muller, Jan Ben, Eric Cosatto, Beat Flepp, and Yann L Cun · 2006
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End to end learning for self-driving cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al · 2016
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Continuous deep q-learning with model-based acceleration
Shixiang Gu, Timothy Lillicrap, Ilya Sutskever, and Sergey Levine · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
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Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2016
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CARLA: An open urban driving simulator
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun · 2017
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Uncertainty-driven imagination for continuous deep reinforcement learning
Gabriel Kalweit and Joschka Boedecker · 2017
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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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Value prediction network
Junhyuk Oh, Satinder Singh, and Honglak Lee · 2017
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The kinematic bicycle model: A consistent model for planning feasible trajectories for autonomous vehicles?
P. Polack, F. Altché, B. d’Andréa-Novel, and A. de La Fortelle · 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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Rates of motor vehicle crashes, injuries and deaths in relation to driver age, united states, 2014-2015
Brian Tefft · 2017
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Sample-efficient reinforcement learning with stochastic ensemble value expansion
Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, and Honglak Lee · 2018
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End-to-end driving via conditional imitation learning
Felipe Codevilla, Matthias Müller, Antonio López, Vladlen Koltun, and Alexey Dosovitskiy · 2018
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Implicit quantile networks for distributional reinforcement learning
Will Dabney, Georg Ostrovski, David Silver, and Rémi Munos · 2018
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Discovering and removing exogenous state variables and rewards for reinforcement learning
Chauffeurnet: Learning to drive by imitating the best and synthesizing the worst
Mayank Bansal, Alex Krizhevsky, and Abhijit Ogale · 2019
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Learning by cheating
Dian Chen, Brady Zhou, Vladlen Koltun, and Philipp Krähenbühl · 2019
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Leveraging procedural generation to benchmark reinforcement learning
Karl Cobbe, Christopher Hesse, Jacob Hilton, and John Schulman · 2019
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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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Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi · 2019
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End-to-end interpretable neural motion planner
Wenyuan Zeng, Wenjie Luo, Simon Suo, Abbas Sadat, Bin Yang, Sergio Casas, and Raquel Urtasun · 2019
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Thomas Dietterich, George Trimponias, and Zhitang Chen · 2018
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Model-based value estimation for efficient model-free reinforcement learning
Vladimir Feinberg, Alvin Wan, Ion Stoica, Michael I Jordan, Joseph E Gonzalez, and Sergey Levine · 2018
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Recurrent world models facilitate policy evolution
David Ha and Jürgen Schmidhuber · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Rainbow: Combining improvements in deep reinforcement learning
Matteo Hessel, Joseph Modayil, Hado Van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, and David Silver · 2018
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Model-ensemble trust-region policy optimization
Thanard Kurutach, Ignasi Clavera, Yan Duan, Aviv Tamar, and Pieter Abbeel · 2018
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Cirl: Controllable imitative reinforcement learning for vision-based self-driving
Xiaodan Liang, Tairui Wang, Luona Yang, and Eric Xing · 2018
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Learning compact models for planning with exogenous processes
Rohan Chitnis and Tomás Lozano-Pérez · 2020
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Phasic policy gradient
Karl Cobbe, Jacob Hilton, Oleg Klimov, and John Schulman · 2020
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Learning quadrupedal locomotion over challenging terrain
Joonho Lee, Jemin Hwangbo, Lorenz Wellhausen, Vladlen Koltun, and Marco Hutter · 2020
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Perceive, predict, and plan: Safe motion planning through interpretable semantic representations
Abbas Sadat, Sergio Casas, Mengye Ren, Xinyu Wu, Pranaab Dhawan, and Raquel Urtasun · 2020
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Mastering atari, go, chess and shogi by planning with a learned model
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, et al · 2020
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Scalability in perception for autonomous driving: Waymo open dataset
Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, et al · 2020
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End-to-end model-free reinforcement learning for urban driving using implicit affordances
Marin Toromanoff, Emilie Wirbel, and Fabien Moutarde · 2020
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Mp3: A unified model to map, perceive, predict and plan
Sergio Casas, Abbas Sadat, and Raquel Urtasun · 2021
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Multi-modal fusion transformer for end-to-end autonomous driving
Aditya Prakash, Kashyap Chitta, and Andreas Geiger · 2021
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