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The recent offline reinforcement learning (RL) studies have achieved much progress to make RL usable in real-world systems by learning policies from pre-collected datasets without environment interaction.
The cross-entropy method for optimization
Zdravko I Botev, Dirk P Kroese, Reuven Y Rubinstein, and Pierre L’Ecuyer · 2013
Earlier work this paper cites.
Made: Masked autoencoder for distribution estimation
Mathieu Germain, Karol Gregor, Iain Murray, and Hugo Larochelle · 2015
Earlier work this paper cites.
End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
Earlier work this paper cites.
Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
Earlier work this paper cites.
Model predictive path integral control: From theory to parallel computation
Grady Williams, Andrew Aldrich, and Evangelos A Theodorou · 2017
Earlier work this paper cites.
Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Kurtland Chua, Roberto Calandra, Rowan McAllister, and Sergey Levine · 2018
Earlier work this paper cites.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 2019
Earlier work this paper cites.
When to trust your model: Model-based policy optimization
Michael Janner, Justin Fu, Marvin Zhang, and Sergey Levine · 2019
Earlier work this paper cites.
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.
Batch policy learning under constraints
Hoang M Le, Cameron Voloshin, and Yisong Yue · 2019
Cited alongside, same era.
Plan online, learn offline: Efficient learning and exploration via model-based control
Kendall Lowrey, Aravind Rajeswaran, Sham Kakade, Emanuel Todorov, and Igor Mordatch · 2019
Cited alongside, same era.
Behavior regularized offline reinforcement learning
Yifan Wu, George Tucker, and Ofir Nachum · 2019
Cited alongside, same era.
The importance of pessimism in fixed-dataset policy optimization
Jacob Buckman, Carles Gelada, and Marc G Bellemare · 2020
Cited alongside, same era.
Exploring model-based planning with policy networks
Tingwu Wang and Jimmy Ba · 2020
Later among the works it cites.
Mopo: Model-based offline policy optimization
Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Zou, Sergey Levine, Chelsea Finn, and Tengyu Ma · 2020
Later among the works it cites.
Model-based offline planning
Arthur Argenson and Gabriel Dulac-Arnold · 2021
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A minimalist approach to offline reinforcement learning
Scott Fujimoto and Shixiang Shane Gu · 2021
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Emaq: Expected-max q-learning operator for simple yet effective offline and online rl
Seyed Kamyar Seyed Ghasemipour, Dale Schuurmans, and Shixiang Shane Gu · 2021
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Offline reinforcement learning with fisher divergence critic regularization
Ilya Kostrikov, Rob Fergus, Jonathan Tompson, and Ofir Nachum · 2021
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Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
Cited alongside, same era.
Morel: Model-based offline reinforcement learning
Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, and Thorsten Joachims · 2020
Cited alongside, same era.
Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2020
Cited alongside, same era.
Provably good batch off-policy reinforcement learning without great exploration
Yao Liu, Adith Swaminathan, Alekh Agarwal, and Emma Brunskill · 2020
Cited alongside, same era.
Deep dynamics models for learning dexterous manipulation
Anusha Nagabandi, Kurt Konolige, Sergey Levine, and Vikash Kumar · 2020
Cited alongside, same era.
Offline reinforcement learning with soft behavior regularization
Haoran Xu, Xianyuan Zhan, Jianxiong Li, and Honglei Yin · 2021
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Constraints penalized q-learning for safe offline reinforcement learning
Haoran Xu, Xianyuan Zhan, and Xiangyu Zhu · 2022
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Deepthermal: Combustion optimization for thermal power generating units using offline reinforcement learning
Xianyuan Zhan, Haoran Xu, Yue Zhang, Yusen Huo, Xiangyu Zhu, Honglei Yin, and Yu Zheng · 2022
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