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Offline reinforcement learning (RL) aims to find performant policies from logged data without further environment interaction.
Backpropagation through time: what it does and how to do it
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Richard S Sutton · 1991
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Richard S Sutton, David McAllester, Satinder Singh, and Yishay Mansour · 1999
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Informing sequential clinical decision-making through reinforcement learning: an empirical study
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Batch reinforcement learning
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Human-level control through deep reinforcement learning
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High-dimensional continuous control using generalized advantage estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel · 2016
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Kirthevasan Kandasamy, Yoram Bachrach, Ryota Tomioka, Daniel Tarlow, and David Carter · 2017
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Robust adversarial reinforcement learning
Lerrel Pinto, James Davidson, Rahul Sukthankar, and Abhinav Gupta · 2017
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Reinforcement learning under model mismatch
Aurko Roy, Huan Xu, and Sebastian Pokutta · 2017
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Mastering the game of Go without human knowledge
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Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Kurtland Chua, Roberto Calandra, Rowan McAllister, and Sergey Levine · 2018
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Recurrent world models facilitate policy evolution
David Ha and Jürgen Schmidhuber · 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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Model-ensemble trust-region policy optimization
Thanard Kurutach, Ignasi Clavera, Yan Duan, Aviv Tamar, and Pieter Abbeel · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 2019
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When to trust your model: Model-based policy optimization
Michael Janner, Justin Fu, Marvin Zhang, and Sergey Levine · 2019
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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
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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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Off-policy policy gradient with state distribution correction
Yao Liu, Adith Swaminathan, Alekh Agarwal, and Emma Brunskill · 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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Counterfactual off-policy evaluation with gumbel-max structural causal models
Michael Oberst and David Sontag · 2019
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Solving rubik’s cube with a robot hand
OpenAI, Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, Jonas Schneider, Nikolas Tezak, Jerry Tworek, Peter Welinder, Lilian Weng, Qiming Yuan, Wojciech Zaremba, and Lei Zhang · 2019
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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 Roberts · 2021
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Offline RL without off-policy evaluation
David Brandfonbrener, Will Whitney, Rajesh Ranganath, and Joan Bruna · 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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Combining pessimism with optimism for robust and efficient model-based deep reinforcement learning
Sebastian Curi, Ilija Bogunovic, and Andreas Krause · 2021
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A minimalist approach to offline reinforcement learning
Scott Fujimoto and Shixiang Shane Gu · 2021
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Can you trust your model’s uncertainty? Evaluating predictive uncertainty under dataset shift
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
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Risk averse robust adversarial reinforcement learning
Xinlei Pan, Daniel Seita, Yang Gao, and John Canny · 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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Action robust reinforcement learning and applications in continuous control
Chen Tessler, Yonathan Efroni, and Shie Mannor · 2019
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Behavior regularized offline reinforcement learning
Yifan Wu, George Tucker, and Ofir Nachum · 2019
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An optimistic perspective on offline reinforcement learning
Rishabh Agarwal, Dale Schuurmans, and Mohammad Norouzi · 2020
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Model-augmented actor-critic: Backpropagating through paths
Ignasi Clavera, Violet Fu, and Pieter Abbeel · 2020
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Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, Jongseok Lee, Matthias Humt, Jianxiang Feng, Anna Kruspe, Rudolph Triebel, Peter Jung, Ribana Roscher, et al · 2021
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Weighted model estimation for offline model-based reinforcement learning
Toru Hishinuma and Kei Senda · 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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A workflow for offline model-free robotic reinforcement learning
Aviral Kumar, Anikait Singh, Stephen Tian, Chelsea Finn, and Sergey Levine · 2021
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Deployment-efficient reinforcement learning via model-based offline optimization
Tatsuya Matsushima, Hiroki Furuta, Yutaka Matsuo, Ofir Nachum, and Shixiang Gu · 2021
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Learning when-to-treat policies
Xinkun Nie, Emma Brunskill, and Stefan Wager · 2021
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Offline reinforcement learning from images with latent space models
Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, and Chelsea Finn · 2021
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Stable-baselines3: Reliable reinforcement learning implementations
Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, and Noah Dormann · 2021
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Minimax regret optimisation for robust planning in uncertain Markov decision processes
Marc Rigter, Bruno Lacerda, and Nick Hawes · 2021
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Overcoming model bias for robust offline deep reinforcement learning
Phillip Swazinna, Steffen Udluft, and Thomas Runkler · 2021
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Offline reinforcement learning with reverse model-based imagination
Jianhao Wang, Wenzhe Li, Haozhe Jiang, Guangxiang Zhu, Siyuan Li, and Chongjie Zhang · 2021
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Online robust reinforcement learning with model uncertainty
Yue Wang and Shaofeng Zou · 2021
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Bellman-consistent pessimism for offline reinforcement learning
Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, and Alekh Agarwal · 2021
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Pareto policy pool for model-based offline reinforcement learning
Yijun Yang, Jing Jiang, Tianyi Zhou, Jie Ma, and Yuhui Shi · 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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Towards hyperparameter-free policy selection for offline reinforcement learning
Siyuan Zhang and Nan Jiang · 2021
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Adversarially trained actor critic for offline reinforcement learning
Ching-An Cheng, Tengyang Xie, Nan Jiang, and Alekh Agarwal · 2022
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Offline reinforcement learning with implicit Q-learning
Ilya Kostrikov, Ashvin Nair, and Sergey Levine · 2022
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Revisiting design choices in offline model based reinforcement learning
Cong Lu, Philip Ball, Jack Parker-Holder, Michael Osborne, and Stephen J Roberts · 2022
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Pessimistic model-based offline reinforcement learning under partial coverage
Masatoshi Uehara and Wen Sun · 2022
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