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A major challenge in real-world reinforcement learning (RL) is the sparsity of reward feedback.
Behavior regularized offline reinforcement learning
Yifan Wu, George Tucker, and Ofir Nachum · 1911
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Learning from demonstration
Stefan Schaal et al · 1997
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Algorithms for inverse reinforcement learning
Andrew Y. Ng and Stuart J. Russell · 2000
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Approximately optimal approximate reinforcement learning
Sham M. Kakade and John Langford · 2002
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Maximum entropy inverse reinforcement learning
Brian D. Ziebart, Andrew L. Maas, J. Andrew Bagnell, and Anind K. Dey · 2002
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Design and use paradigms for gazebo, an open-source multi-robot simulator
Nathan P. Koenig and Andrew Howard · 2004
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Apprenticeship learning using linear programming
Umar Syed, Michael Bowling, and Robert E Schapire · 2008
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey J. Gordon, and Drew Bagnell · 2011
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Learning from limited demonstrations
Beomjoon Kim, Amir-massoud Farahmand, Joelle Pineau, and Doina Precup · 2013
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Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio · 2014
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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
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Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 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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Constrained policy optimization
Joshua Achiam, David Held, Aviv Tamar, and Pieter Abbeel · 2017
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Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards
Mel Vecerik, Todd Hester, Jonathan Scholz, Fumin Wang, Olivier Pietquin, Bilal Piot, Nicolas Heess, Thomas Rothörl, Thomas Lampe, and Martin Riedmiller · 2017
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Turtlebot 3 as a robotics education platform
Robin Amsters and Peter Slaets · 2019
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Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 2019
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Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning
Abhishek Gupta, Vikash Kumar, Corey Lynch, Sergey Levine, and Karol Hausman · 2019
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Stabilizing off-policy q-learning via bootstrapping error reduction
Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, and Sergey Levine · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Yang Gao, Huazhe 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
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Deep q-learning from demonstrations
Todd Hester, Matej Vecerík, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Ian Osband, Gabriel Dulac-Arnold, John P. Agapiou, Joel Z. Leibo, and Audrunas Gruslys · 2018
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Policy optimization with demonstrations
Bingyi Kang, Zequn Jie, and Jiashi Feng · 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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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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Robotic operating system
Stanford Artificial Intelligence Laboratory et al
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Making efficient use of demonstrations to solve hard exploration problems
Çaglar Gülçehre, Tom Le Paine, Bobak Shahriari, Misha Denil, Matt Hoffman, Hubert Soyer, Richard Tanburn, Steven Kapturowski, Neil C. Rabinowitz, Duncan Williams, Gabriel Barth-Maron, Ziyu Wang, Nando de Freitas, and Worlds Team · 2020
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Reinforcement learning from imperfect demonstrations under soft expert guidance
Mingxuan Jing, Xiaojian Ma, Wenbing Huang, Fuchun Sun, Chao Yang, Bin Fang, and Huaping Liu · 2020
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Accelerating online reinforcement learning with offline datasets
Ashvin Nair, Murtaza Dalal, Abhishek Gupta, and Sergey Levine · 2020
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Keep doing what worked: Behavior modelling priors for offline reinforcement learning
Noah Siegel, Jost Tobias Springenberg, Felix Berkenkamp, Abbas Abdolmaleki, Michael Neunert, Thomas Lampe, Roland Hafner, Nicolas Heess, and Martin Riedmiller · 2020
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Guided exploration with proximal policy optimization using a single demonstration
Gabriele Libardi, Gianni De Fabritiis, and Sebastian Dittert · 2021
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