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Model-based algorithms, which learn a dynamics model from logged experience and perform some sort of pessimistic planning under the learned model, have emerged as a promising paradigm for offline reinforcement learning (offline RL).
Dyna, an integrated architecture for learning, planning, and reacting
Richard S Sutton · 1991
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Neuro-Dynamic Programming
Dimitri P. Bertsekas and John N. Tsitsiklis · 1996
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Reinforcement Learning: An Introduction
R. S. Sutton and A. G. Barto · 1998
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Off-policy temporal-difference learning with function approximation
Doina Precup, Richard S Sutton, and Sanjoy Dasgupta · 2001
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Tree-based batch mode reinforcement learning
Damien Ernst, Pierre Geurts, and Louis Wehenkel · 2005
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Neural fitted q iteration–first experiences with a data efficient neural reinforcement learning method
Martin Riedmiller · 2005
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Mopo: Model-based offline policy optimization
Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Zou, Sergey Levine, Chelsea Finn, and Tengyu Ma · 2005
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Finite-time bounds for fitted value iteration
Rémi Munos and Csaba Szepesvari · 2008
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Near-optimal regret bounds for reinforcement learning
Thomas Jaksch, Ronald Ortner, and Peter Auer · 2010
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Informing sequential clinical decision-making through reinforcement learning: an empirical study
Susan M Shortreed, Eric Laber, Daniel J Lizotte, T Scott Stroup, Joelle Pineau, and Susan A Murphy · 2011
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Thomas Degris, Martha White, and Richard S Sutton · 2012
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Batch reinforcement learning
Sascha Lange, Thomas Gabel, and Martin A. Riedmiller · 2012
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Agnostic system identification for model-based reinforcement learning
Stephane Ross and Drew Bagnell · 2012
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Batch learning from logged bandit feedback through counterfactual risk minimization
Adith Swaminathan and Thorsten Joachims · 2015
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Safe policy improvement by minimizing robust baseline regret
Marek Petrik, Yinlam Chow, and Mohammad Ghavamzadeh · 2016
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An emphatic approach to the problem of off-policy temporal-difference learning
Richard S Sutton, A Rupam Mahmood, and Martha White · 2016
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Deep visual foresight for planning robot motion
Chelsea Finn and Sergey Levine · 2017
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Why is posterior sampling better than optimism for reinforcement learning?
Ian Osband and Benjamin Van Roy · 2017
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Efficient exploration through bayesian deep q-networks
Kamyar Azizzadenesheli, Emma Brunskill, and Animashree Anandkumar · 2018
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Visual foresight: Model-based deep reinforcement learning for vision-based robotic control
Frederik Ebert, Chelsea Finn, Sudeep Dasari, Annie Xie, Alex Lee, and Sergey Levine · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Composable action-conditioned predictors: Flexible off-policy learning for robot navigation
Gregory Kahn, Adam Villaflor, Pieter Abbeel, and Sergey Levine · 2018
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Scalable deep reinforcement learning for vision-based robotic manipulation
Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, et al · 2018
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Randomized prior functions for deep reinforcement learning
Ian Osband, John Aslanides, and Albin Cassirer · 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, et al · 2018
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Supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation
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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Stochastic latent actor-critic: Deep reinforcement learning with a latent variable model
Alex X. Lee, Anusha Nagabandi, Pieter Abbeel, 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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Provably good batch reinforcement learning without great exploration
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L. Wang, Wei Zhang, Xiaofeng He, and H. Zha · 2018
Cited alongside, same era.
Reinforcement learning: Theory and algorithms
Alekh Agarwal, Nan Jiang, and Sham M Kakade · 2019
Cited alongside, same era.
International conference on machine learning
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2019
Cited alongside, same era.
When to trust your model: Model-based policy optimization
Michael Janner, Justin Fu, Marvin Zhang, and Sergey Levine · 2019
Cited alongside, same era.
Way off-policy batch deep reinforcement learning of implicit human preferences in dialog
Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Gu, and Rosalind Picard · 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
Cited alongside, same era.
Safe policy improvement with baseline bootstrapping
Romain Laroche, Paul Trichelair, and Remi Tachet Des Combes · 2019
Cited alongside, same era.
Off-policy policy gradient with state distribution correction
Yao Liu, Adith Swaminathan, Alekh Agarwal, and Emma Brunskill · 2019
Cited alongside, same era.
Yao Liu, Adith Swaminathan, Alekh Agarwal, and Emma Brunskill · 2020
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Iris: Implicit reinforcement without interaction at scale for learning control from offline robot manipulation data
Ajay Mandlekar, Fabio Ramos, Byron Boots, Silvio Savarese, Li Fei-Fei, Animesh Garg, and Dieter Fox · 2020
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Deployment-efficient reinforcement learning via model-based offline optimization
Tatsuya Matsushima, Hiroki Furuta, Yutaka Matsuo, Ofir Nachum, and Shixiang Gu · 2020
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Offline reinforcement learning from images with latent space models
Rafael Rafailov, Tianhe Yu, A. Rajeswaran, and Chelsea Finn · 2020
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Keep doing what worked: Behavioral modelling priors for offline reinforcement learning
Noah Y Siegel, Jost Tobias Springenberg, Felix Berkenkamp, Abbas Abdolmaleki, Michael Neunert, Thomas Lampe, Roland Hafner, and Martin Riedmiller · 2020
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Cog: Connecting new skills to past experience with offline reinforcement learning
Avi Singh, Albert Yu, Jonathan Yang, Jesse Zhang, Aviral Kumar, and Sergey Levine · 2020
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Overcoming model bias for robust offline deep reinforcement learning
Phillip Swazinna, Steffen Udluft, and Thomas Runkler · 2020
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Bdd100k: A diverse driving dataset for heterogeneous multitask learning
F. Yu, H. Chen, X. Wang, Wenqi Xian, Yingying Chen, Fangchen Liu, V. Madhavan, and Trevor Darrell · 2020
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Plas: Latent action space for offline reinforcement learning
Wenxuan Zhou, Sujay Bajracharya, and David Held · 2020
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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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Is pessimism provably efficient for offline rl?
Ying Jin, Zhuoran Yang, and Zhaoran Wang · 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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Representation balancing offline model-based reinforcement learning
Byung-Jun Lee, Jongmin Lee, and Kee-Eung Kim · 2021
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Bridging offline reinforcement learning and imitation learning: A tale of pessimism
Paria Rashidinejad, Banghua Zhu, Cong Ma, Jiantao Jiao, and Stuart Russell · 2021
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S4rl: Surprisingly simple self-supervision for offline reinforcement learning
Samarth Sinha and Animesh Garg · 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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Model-based offline planning with trajectory pruning
Xianyuan Zhan, Xiangyu Zhu, and Haoran Xu · 2021
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