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Offline reinforcement learning algorithms promise to be applicable in settings where a fixed dataset is available and no new experience can be acquired.
Dualdice: Behavior-agnostic estimation of discounted stationary distribution corrections
Ofir Nachum, Yinlam Chow, Bo Dai, and Lihong Li · 1906
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If maxent RL is the answer, what is the question?
Benjamin Eysenbach and Sergey Levine · 1910
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Advantage-weighted regression: Simple and scalable off-policy reinforcement learning
Xue Bin Peng, Aviral Kumar, Grace Zhang, and Sergey Levine · 1910
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Behavior regularized offline reinforcement learning
Yifan Wu, George Tucker, and Ofir Nachum · 1911
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Algaedice: Policy gradient from arbitrary experience
Ofir Nachum, Bo Dai, Ilya Kostrikov, Yinlam Chow, Lihong Li, and Dale Schuurmans · 1912
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Foot trajectory for a quadruped walking machine
Y. Sakakibara, K. Kan, Y. Hosoda, M. Hattori, and M. Fujie · 1990
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Integrated architectures for learning, planning, and reacting based on approximating dynamic programming
Richard S. Sutton · 1990
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Reinforcement learning via fenchel-rockafellar duality
Ofir Nachum and Bo Dai · 2001
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D4RL: datasets for deep data-driven reinforcement learning
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2004
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Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2005
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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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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2009
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Relative entropy policy search
Jan Peters, Katharina Mulling, and Yasemin Altun · 2010
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Batch reinforcement learning
Sascha Lange, Thomas Gabel, and Martin Riedmiller · 2012
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Domain randomization for transferring deep neural networks from simulation to the real world
Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel · 2017
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Maximum a posteriori policy optimisation
Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Rémi Munos, Nicolas Heess, and Martin A. Riedmiller · 2018
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Soft actor-critic algorithms and applications
Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, and Sergey Levine · 2018
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Policies modulating trajectory generators
Atil Iscen, Ken Caluwaerts, Jie Tan, Tingnan Zhang, Erwin Coumans, Vikas Sindhwani, and Vincent Vanhoucke · 2018
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Reinforcement learning and control as probabilistic inference: Tutorial and review
Sergey Levine · 2018
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Breaking the curse of horizon: Infinite-horizon off-policy estimation
Qiang Liu, Lihong Li, Ziyang Tang, and Dengyong Zhou · 2018
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Sim-to-real transfer of robotic control with dynamics randomization
Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2018
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Information-theoretic considerations in batch reinforcement learning
Jinglin Chen and Nan Jiang · 2019
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Bail: Best-action imitation learning for batch deep reinforcement learning
Xinyue Chen, Zijian Zhou, Zheng Wang, Che Wang, Yanqiu Wu, and Keith Ross · 2019
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Off-policy evaluation via the regularized lagrangian
Mengjiao Yang, Ofir Nachum, Bo Dai, Lihong Li, and Dale Schuurmans · 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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Gendice: Generalized offline estimation of stationary values
Ruiyi Zhang, Bo Dai, Lihong Li, and Dale Schuurmans · 2020
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Augmented world models facilitate zero-shot dynamics generalization from a single offline environment
Philip J. Ball, Cong Lu, Jack Parker-Holder, and Stephen J. Roberts · 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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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
Cited alongside, same era.
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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Minimax value interval for off-policy evaluation and policy optimization
Nan Jiang and Jiawei Huang · 2020
Cited alongside, same era.
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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One solution is not all you need: Few-shot extrapolation via structured maxent RL
Saurabh Kumar, Aviral Kumar, Sergey Levine, and Chelsea Finn · 2020
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Actionable models: Unsupervised offline reinforcement learning of robotic skills
Yevgen Chebotar, Karol Hausman, Yao Lu, Ted Xiao, Dmitry Kalashnikov, Jacob Varley, Alex Irpan, Benjamin Eysenbach, Ryan Julian, Chelsea Finn, and Sergey Levine · 2021
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Pybullet, a python module for physics simulation for games, robotics and machine learning
Erwin Coumans and Yunfei Bai · 2021
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Off-dynamics reinforcement learning: Training for transfer with domain classifiers
Benjamin Eysenbach, Shreyas Chaudhari, Swapnil Asawa, Sergey Levine, and Ruslan Salakhutdinov · 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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Discriminator augmented model-based reinforcement learning
Behzad Haghgoo, Allan Zhou, Archit Sharma, and Chelsea Finn · 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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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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Offline meta-reinforcement learning with advantage weighting
Eric Mitchell, Rafael Rafailov, Xue Bin Peng, Sergey Levine, and Chelsea Finn · 2021
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Neorl: A near real-world benchmark for offline reinforcement learning
Rongjun Qin, Songyi Gao, Xingyuan Zhang, Zhen Xu, Shengkai Huang, Zewen Li, Weinan Zhang, and Yang Yu · 2021
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Constraints penalized q-learning for safe offline reinforcement learning
Haoran Xu, Xianyuan Zhan, and Xiangyu Zhu · 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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Terrain-aware risk-assessment-network-aided deep reinforcement learning for quadrupedal locomotion in tough terrain
Hongyin Zhang, Jilong Wang, Zhengqing Wu, Yinuo Wang, and Donglin Wang · 2021
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