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Most prior approaches to offline reinforcement learning (RL) have taken an iterative actor-critic approach involving off-policy evaluation.
Modified policy iteration algorithms for discounted markov decision problems
Martin L Puterman and Moon Chirl Shin · 1978
Earlier work this paper cites.
Approximately optimal approximate reinforcement learning
Sham Kakade and John Langford · 2002
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Approximate modified policy iteration
Bruno Scherrer, Victor Gabillon, Mohammad Ghavamzadeh, and Matthieu Geist · 2012
Earlier work this paper cites.
The truncated normal distribution, 2014
John Burkhardt · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
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 · 2015
Earlier work this paper cites.
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, et al · 2015
Earlier work this paper cites.
Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
Earlier work this paper cites.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Earlier work this paper cites.
Safe and efficient off-policy reinforcement learning
Rémi Munos, Tom Stepleton, Anna Harutyunyan, and Marc Bellemare · 2016
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Deep reinforcement learning with double q-learning
Hado Van Hasselt, Arthur Guez, and David Silver · 2016
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Constrained policy optimization
Joshua Achiam, David Held, Aviv Tamar, and Pieter Abbeel · 2017
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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 · 2017
Earlier work this paper cites.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
Earlier work this paper cites.
Exponentially weighted imitation learning for batched historical data
Qing Wang, Jiechao Xiong, Lei Han, Han Liu, Tong Zhang, et al · 2018
Cited alongside, same era.
Reinforcement learning: Theory and algorithms
Alekh Agarwal, Nan Jiang, and S. Kakade · 2019
Cited alongside, same era.
Information-theoretic considerations in batch reinforcement learning
Jinglin Chen and Nan Jiang · 2019
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Benchmarking batch deep reinforcement learning algorithms
Scott Fujimoto, Edoardo Conti, Mohammad Ghavamzadeh, and Joelle Pineau · 2019
Cited alongside, same era.
Way off-policy batch deep reinforcement learning of implicit human preferences in dialog, 2019
Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Gu, and Rosalind Picard · 2019
Cited alongside, same era.
Bail: Best-action imitation learning for batch deep reinforcement learning
Xinyue Chen, Zijian Zhou, Zheng Wang, Che Wang, Yanqiu Wu, and Keith Ross · 2020
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Minimax-optimal off-policy evaluation with linear function approximation
Yaqi Duan, Zeyu Jia, and Mengdi Wang · 2020
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D4rl: Datasets for deep data-driven reinforcement learning
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
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You only evaluate once – a simple baseline algorithm for offline rl
Wonjoon Goo and Scott Niekum · 2020
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Rl unplugged: Benchmarks for offline reinforcement learning
Caglar Gulcehre, Ziyu Wang, Alexander Novikov, Tom Le Paine, Sergio Gómez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel Mankowitz, Cosmin Paduraru, et al · 2020
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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
Cited alongside, same era.
Safe policy improvement with baseline bootstrapping
Romain Laroche, Paul Trichelair, and Remi Tachet Des Combes · 2019
Cited alongside, same era.
Algaedice: Policy gradient from arbitrary experience
Ofir Nachum, Bo Dai, Ilya Kostrikov, Yinlam Chow, Lihong Li, and Dale Schuurmans · 2019
Cited alongside, same era.
Safe policy improvement with soft baseline bootstrapping, 2019
Kimia Nadjahi, Romain Laroche, and Rémi Tachet des Combes · 2019
Cited alongside, same era.
Advantage-weighted regression: Simple and scalable off-policy reinforcement learning
Xue Bin Peng, Aviral Kumar, Grace Zhang, and Sergey Levine · 2019
Cited alongside, same era.
Empirical study of off-policy policy evaluation for reinforcement learning
Cameron Voloshin, Hoang M Le, Nan Jiang, and Yisong Yue · 2019
Cited alongside, same era.
Behavior regularized offline reinforcement learning, 2019
Yifan Wu, George Tucker, and Ofir Nachum · 2019
Cited alongside, same era.
Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 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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Hyperparameter selection for offline reinforcement learning, 2020
Tom Le Paine, Cosmin Paduraru, Andrea Michi, Caglar Gulcehre, Konrad Zolna, Alexander Novikov, Ziyu Wang, and Nando de Freitas · 2020
Later among the works it cites.
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
Later among the works it cites.
Regularized behavior value estimation
Caglar Gulcehre, Sergio Gómez Colmenarejo, Ziyu Wang, Jakub Sygnowski, Thomas Paine, Konrad Zolna, Yutian Chen, Matthew Hoffman, Razvan Pascanu, and Nando de Freitas · 2021
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Offline reinforcement learning with fisher divergence critic regularization
Ilya Kostrikov, Jonathan Tompson, Rob Fergus, and Ofir Nachum · 2021
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Instabilities of offline rl with pre-trained neural representation
Ruosong Wang, Yifan Wu, Ruslan Salakhutdinov, and Sham M Kakade · 2021
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Exponential lower bounds for batch reinforcement learning: Batch rl can be exponentially harder than online rl, 2021
Andrea Zanette · 2021
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