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Offline reinforcement learning (RL) aims to learn a policy using only pre-collected and fixed data.
D4RL: datasets for deep data-driven reinforcement learning
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2004
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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
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Learning structured output representation using deep conditional generative models
Kihyuk Sohn, Honglak Lee, and Xinchen Yan · 2015
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Mutual alignment transfer learning
Markus Wulfmeier, Ingmar Posner, and Pieter Abbeel · 2017
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Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 2019
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A theory of regularized markov decision processes
Matthieu Geist, Bruno Scherrer, and Olivier Pietquin · 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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Algaedice: Policy gradient from arbitrary experience
Ofir Nachum, Bo Dai, Ilya Kostrikov, Yinlam Chow, Lihong Li, and Dale Schuurmans · 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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Behavior regularized offline reinforcement learning
Yifan Wu, George Tucker, and Ofir Nachum · 2019
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Off-dynamics reinforcement learning: Training for transfer with domain classifiers
Benjamin Eysenbach, Swapnil Asawa, Shreyas Chaudhari, Sergey Levine, and Ruslan Salakhutdinov · 2020
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Morel: Model-based offline reinforcement learning
Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, and Thorsten Joachims · 2020
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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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Mopo: Model-based offline policy optimization
Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Y Zou, Sergey Levine, Chelsea Finn, and Tengyu Ma · 2020
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Uncertainty-based offline reinforcement learning with diversified q-ensemble
Gaon An, Seungyong Moon, Jang-Hyun Kim, and Hyun Oh Song · 2021
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Behavioral priors and dynamics models: Improving performance and domain transfer in offline rl
Catherine Cang, Aravind Rajeswaran, Pieter Abbeel, and Michael Laskin · 2021
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Actionable models: Unsupervised offline reinforcement learning of robotic skills
Yevgen Chebotar, Karol Hausman, Yao Lu, Ted Xiao, Dmitry Kalashnikov, Jake Varley, Alex Irpan, Benjamin Eysenbach, Ryan Julian, Chelsea Finn, et al · 2021
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Decision transformer: Reinforcement learning via sequence modeling
Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Misha Laskin, Pieter Abbeel, Aravind Srinivas, and Igor Mordatch · 2021
Cited alongside, same era.
Mismatched no more: Joint model-policy optimization for model-based rl
Unsupervised discovery of transitional skills for deep reinforcement learning
Qiangxing Tian, Jinxin Liu, Guanchu Wang, and Donglin Wang · 2021
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Robust inverse reinforcement learning under transition dynamics mismatch
Luca Viano, Yu-Ting Huang, Parameswaran Kamalaruban, Adrian Weller, and Volkan Cevher · 2021
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Uncertainty weighted actor-critic for offline reinforcement learning
Yue Wu, Shuangfei Zhai, Nitish Srivastava, Joshua Susskind, Jian Zhang, Ruslan Salakhutdinov, and Hanlin Goh · 2021
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Combo: Conservative offline model-based policy optimization
Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, and Chelsea Finn · 2021
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Pessimistic bootstrapping for uncertainty-driven offline reinforcement learning
Chenjia Bai, Lingxiao Wang, Zhuoran Yang, Zhihong Deng, Animesh Garg, Peng Liu, and Zhaoran Wang · 2022
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Benjamin Eysenbach, Alexander Khazatsky, Sergey Levine, and Ruslan Salakhutdinov · 2021
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Cross-domain imitation learning via optimal transport
Arnaud Fickinger, Samuel Cohen, Stuart Russell, and Brandon Amos · 2021
Cited alongside, same era.
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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Weighted model estimation for offline model-based reinforcement learning
Toru Hishinuma and Kei Senda · 2021
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Off-dynamics inverse reinforcement learning from hetero-domain
Yachen Kang, Jinxin Liu, Xin Cao, and Donglin Wang · 2021
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Offline reinforcement learning with implicit q-learning
Ilya Kostrikov, Ashvin Nair, and Sergey Levine · 2021
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Unsupervised domain adaptation with dynamics-aware rewards in reinforcement learning
Jinxin Liu, Hao Shen, Donglin Wang, Yachen Kang, and Qiangxing Tian · 2021
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Learn what matters: cross-domain imitation learning with task-relevant embeddings
Tim Franzmeyer, Philip HS Torr, and João F Henriques · 2022
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A review of safe reinforcement learning: Methods, theory and applications
Shangding Gu, Long Yang, Yali Du, Guang Chen, Florian Walter, Jun Wang, Yaodong Yang, and Alois Knoll · 2022
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Mildly conservative q-learning for offline reinforcement learning
Jiafei Lyu, Xiaoteng Ma, Xiu Li, and Zongqing Lu · 2022
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Supported policy optimization for offline reinforcement learning
Jialong Wu, Haixu Wu, Zihan Qiu, Jianmin Wang, and Mingsheng Long · 2022
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Regularizing a model-based policy stationary distribution to stabilize offline reinforcement learning
Shentao Yang, Yihao Feng, Shujian Zhang, and Mingyuan Zhou · 2022
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Discriminator-guided model-based offline imitation learning
Wenjia Zhang, Haoran Xu, Haoyi Niu, Peng Cheng, Ming Li, Heming Zhang, Guyue Zhou, and Xianyuan Zhan · 2022
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Beyond reward: Offline preference-guided policy optimization
Yachen Kang, Diyuan Shi, Jinxin Liu, Li He, and Donglin Wang · 2023
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Chipformer: Transferable chip placement via offline decision transformer
Yao Lai, Jinxin Liu, Zhentao Tang, Bin Wang, HAO Jianye, and Ping Luo · 2023
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DROP: Conservative model-based optimization for offline reinforcement learning, 2023
Jinxin Liu, Hongyin Zhang, Zifeng Zhuang, Yachen Kang, Donglin Wang, Bin Wang, and Jianye HAO · 2023
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Behavior proximal policy optimization
Zifeng Zhuang, Kun Lei, Jinxin Liu, Donglin Wang, and Yilang Guo · 2023
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