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During recent years, deep reinforcement learning (DRL) has made successful incursions into complex decision-making applications such as robotics, autonomous driving or video games.
Aviral Kumar, Xue Bin Peng, and Sergey Levine · 1912
Earlier work this paper cites.
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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The open motion planning library
Ioan A Sucan, Mark Moll, and Lydia E Kavraki · 2012
Earlier work this paper cites.
V-rep: A versatile and scalable robot simulation framework
Eric Rohmer, Surya PN Singh, and Marc Freese · 2013
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Bullet physics simulation
Erwin Coumans · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Deep spatial autoencoders for visuomotor learning
Chelsea Finn, Xin Yu Tan, Yan Duan, Trevor Darrell, Sergey Levine, and Pieter Abbeel · 2016
Earlier work this paper cites.
Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
Earlier work this paper cites.
Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver · 2016
Earlier work this paper cites.
Hindsight experience replay
Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, OpenAI Pieter Abbeel, and Wojciech Zaremba · 2017
Earlier work this paper cites.
Deep visual foresight for planning robot motion
Chelsea Finn and Sergey Levine · 2017
Earlier work this paper cites.
Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, John Schulman, Emanuel Todorov, and Sergey Levine · 2017
Earlier work this paper cites.
Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards
Matej Vecerík, Todd Hester, Jonathan Scholz, Fumin Wang, Olivier Pietquin, Bilal Piot, Nicolas Heess, Thomas Rothörl, Thomas Lampe, and Martin A. Riedmiller · 2017
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Neural task programming: Learning to generalize across hierarchical tasks
Danfei Xu, Suraj Nair, Yuke Zhu, Julian Gao, Animesh Garg, Li Fei-Fei, and Silvio Savarese · 2017
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Reinforcement learning from imperfect demonstrations, 2018
Yang Gao, Huazhe(Harry) Xu, Ji Lin, Fisher Yu, Sergey Levine, and Trevor Darrell · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor, 2018
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation
Implementation matters in deep rl: A case study on ppo and trpo
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, and Aleksander Madry · 2020
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Rewriting history with inverse rl: Hindsight inference for policy improvement
Benjamin Eysenbach, Xinyang Geng, Sergey Levine, and Ruslan Salakhutdinov · 2020
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Rlbench: The robot learning benchmark & learning environment
Stephen James, Zicong Ma, David Rovick Arrojo, and Andrew J Davison · 2020
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Generalized hindsight for reinforcement learning
Alexander C Li, Lerrel Pinto, and Pieter Abbeel · 2020
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Demonstration actor critic, 2020
Guoqing Liu, Li Zhao, Pushi Zhang, Jiang Bian, Tao Qin, Nenghai Yu, and Tie-Yan Liu · 2020
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Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, et al · 2018
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Overcoming exploration in reinforcement learning with demonstrations
Ashvin Nair, Bob McGrew, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2018
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Self-imitation learning
Junhyuk Oh, Yijie Guo, Satinder Singh, and Honglak Lee · 2018
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Reinforcement and imitation learning for diverse visuomotor skills, 2018
Yuke Zhu, Ziyu Wang, Josh Merel, Andrei Rusu, Tom Erez, Serkan Cabi, Saran Tunyasuvunakool, János Kramár, Raia Hadsell, Nando de Freitas, and Nicolas Heess · 2018
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Learning to reach goals via iterated supervised learning
Dibya Ghosh, Abhishek Gupta, Ashwin Reddy, Justin Fu, Coline Devin, Benjamin Eysenbach, and Sergey Levine · 2019
Cited alongside, same era.
Neural task graphs: Generalizing to unseen tasks from a single video demonstration
De-An Huang, Suraj Nair, Danfei Xu, Yuke Zhu, Animesh Garg, Li Fei-Fei, Silvio Savarese, and Juan Carlos Niebles · 2019
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Solar: Deep structured representations for model-based reinforcement learning
Marvin Zhang, Sharad Vikram, Laura Smith, Pieter Abbeel, Matthew Johnson, and Sergey Levine · 2019
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Guided cost learning: Deep inverse optimal control via policy optimization
Chelsea Finn, Sergey Levine, and Pieter Abbeel
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{SQIL}: Imitation learning via reinforcement learning with sparse rewards
Siddharth Reddy, Anca D. Dragan, and Sergey Levine · 2020
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Learning predictive models from observation and interaction
Karl Schmeckpeper, Annie Xie, Oleh Rybkin, Stephen Tian, Kostas Daniilidis, Sergey Levine, and Chelsea Finn · 2020
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Transporter networks: Rearranging the visual world for robotic manipulation
Andy Zeng, Pete Florence, Jonathan Tompson, Stefan Welker, Jonathan Chien, Maria Attarian, Travis Armstrong, Ivan Krasin, Dan Duong, Vikas Sindhwani, et al · 2020
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Abhishek Gupta, Justin Yu, Tony Z. Zhao, Vikash Kumar, Aaron Rovinsky, Kelvin Xu, Thomas Devlin, and Sergey Levine · 2021
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Coarse-to-fine q-attention: Efficient learning for visual robotic manipulation via discretisation
Stephen James, Kentaro Wada, Tristan Laidlow, and Andrew J Davison · 2021
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Mt-opt: Continuous multi-task robotic reinforcement learning at scale
Dmitry Kalashnikov, Jacob Varley, Yevgen Chebotar, Benjamin Swanson, Rico Jonschkowski, Chelsea Finn, Sergey Levine, and Karol Hausman · 2021
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