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Offline reinforcement learning (RL), also known as batch RL, offers the prospect of policy optimization from large pre-recorded datasets without online environment interaction.
Benchmarking batch deep reinforcement learning algorithms
Scott Fujimoto, Edoardo Conti, Mohammad Ghavamzadeh, and Joelle Pineau · 1910
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Aviral Kumar, Xue Bin Peng, and Sergey Levine · 1912
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ALVINN: An autonomous land vehicle in a neural network
Dean A Pomerleau · 1989
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Policy gradient methods for reinforcement learning with function approximation
Richard S. Sutton, David McAllester, Satinder Singh, and Yishay Mansour · 1999
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Accelerating online reinforcement learning with offline datasets
Ashvin Nair, Murtaza Dalal, Abhishek Gupta, and Sergey Levine · 2006
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Fitted Q-iteration by advantage weighted regression
Gerhard Neumann and Jan R. Peters · 2009
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A reduction of imitation learning and structured prediction to no-regret online learning
Stephane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
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Batch reinforcement learning
Sascha Lange, Thomas Gabel, and Martin Riedmiller · 2012
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin A. Riedmiller · 2014
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Minimax estimation of discrete distributions
Yanjun Han, Jiantao Jiao, and Tsachy Weissman · 2015
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Learning continuous control policies by stochastic value gradients
Nicolas Heess, Gregory Wayne, David Silver, Tim Lillicrap, Tom Erez, and Yuval Tassa · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
Cited alongside, same era.
A distributional perspective on reinforcement learning
Marc G. Bellemare, Will Dabney, and Rémi Munos · 2017
Cited alongside, same era.
Emergence of locomotion behaviours in rich environments
Nicolas Heess, Dhruva Tirumala, Srinivasan Sriram, Jay Lemmon, Josh Merel, Greg Wayne, Yuval Tassa, Tom Erez, Ziyu Wang, S. M. Ali Eslami, Martin A. Riedmiller, and David Silver · 2017
Cited alongside, same era.
Overcoming exploration in reinforcement learning with demonstrations
BAIL: Best-action imitation learning for batch deep reinforcement learning
Xinyue Chen, Zijian Zhou, Zheng Wang, Che Wang, Yanqiu Wu, Qing Deng, and Keith Ross · 2019
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Challenges of real-world reinforcement learning
Gabriel Dulac-Arnold, Daniel J. Mankowitz, and Todd Hester · 2019
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Way off-policy batch deep reinforcement learning of implicit human preferences in dialog
Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, Craig Ferguson, Àgata Lapedriza, Noah Jones, Shixiang Gu, and Rosalind W. Picard · 2019
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Advantage-weighted regression: Simple and scalable off-policy reinforcement learning
Xue Bin Peng, Aviral Kumar, Grace Zhang, and Sergey Levine · 2019
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Ashvin Nair, Bob McGrew, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2017
Cited alongside, same era.
Maximum a posteriori policy optimisation
Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Remi Munos, Nicolas Heess, and Martin Riedmiller · 2018
Cited alongside, same era.
Distributional policy gradients
Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, and Timothy Lillicrap · 2018
Cited alongside, same era.
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, and Demis Hassabis · 2018
Cited alongside, same era.
Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, Timothy Lillicrap, and Martin Riedmiller · 2018
Cited alongside, same era.
Exponentially weighted imitation learning for batched historical data
Qing Wang, Jiechao Xiong, Lei Han, peng sun, Han Liu, and Tong Zhang · 2018
Cited alongside, same era.
Striving for simplicity in off-policy deep reinforcement learning
Rishabh Agarwal, Dale Schuurmans, and Mohammad Norouzi · 2019
Cited alongside, same era.
Scaling data-driven robotics with reward sketching and batch reinforcement learning
Serkan Cabi, Sergio Gómez Colmenarejo, Alexander Novikov, Ksenia Konyushkova, Scott Reed, Rae Jeong, Konrad Zolna, Yusuf Aytar, David Budden, Mel Vecerik, Oleg Sushkov, David Barker, Jonathan Scholz, Misha Denil, Nando de Freitas, and Ziyu Wang · 2019
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Rl unplugged: A suite of benchmarks for offline reinforcement learning
Anonymous · 2020
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Acme: A research framework for distributed reinforcement learning
Matt Hoffman, Bobak Shahriari, John Aslanides, Gabriel Barth-Maron, Feryal Behbahani, Tamara Norman, Abbas Abdolmaleki, Albin Cassirer, Fan Yang, Kate Baumli, Sarah Henderson, Alex Novikov, Sergio Gómez Colmenarejo, Serkan Cabi, Caglar Gulcehre, Tom Le Paine, Andrew Cowie, Ziyu Wang, Bilal Piot, and Nando de Freitas · 2020
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Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
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Deep neuroethology of a virtual rodent
Josh Merel, Diego Aldarondo, Jesse Marshall, Yuval Tassa, Greg Wayne, and Bence Ölveczky · 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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Reinforcement learning in continuous action spaces
Hado van Hasselt and Marco Wiering · 2020
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Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 2062
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