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Offline reinforcement learning enables agents to leverage large pre-collected datasets of environment transitions to learn control policies, circumventing the need for potentially expensive or unsafe online data collection.
Dyna, an integrated architecture for learning, planning, and reacting
Richard S. Sutton · 1991
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David John Cameron Mackay · 1992
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A natural policy gradient
Sham M Kakade · 2001
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Tree-based batch mode reinforcement learning
Damien Ernst, Pierre Geurts, and Louis Wehenkel · 2005
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Hyperparameter selection for offline reinforcement learning
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Visualizing data using t-SNE
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PILCO: A model-based and data-efficient approach to policy search
Marc Peter Deisenroth and Carl Edward Rasmussen · 2011
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Weight uncertainty in neural network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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Illuminating search spaces by mapping elites, 2015
Jean-Baptiste Mouret and Jeff Clune · 2015
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger · 2017
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Population based training of neural networks, 2017
Max Jaderberg, Valentin Dalibard, Simon Osindero, Wojciech M. Czarnecki, Jeff Donahue, Ali Razavi, Oriol Vinyals, Tim Green, Iain Dunning, Karen Simonyan, Chrisantha Fernando, and Koray Kavukcuoglu · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Sample-efficient reinforcement learning with stochastic ensemble value expansion
Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, and Honglak Lee · 2018
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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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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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Accurate uncertainties for deep learning using calibrated regression
Volodymyr Kuleshov, Nathan Fenner, and Stefano Ermon · 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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A systematic comparison of bayesian deep learning robustness in diabetic retinopathy tasks, 2019
Angelos Filos, Sebastian Farquhar, Aidan N. Gomez, Tim G. J. Rudner, Zachary Kenton, Lewis Smith, Milad Alizadeh, Arnoud de Kroon, and Yarin Gal · 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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A simple baseline for bayesian uncertainty in deep learning
Wesley J Maddox, Pavel Izmailov, Timur Garipov, Dmitry P Vetrov, and Andrew Gordon Wilson · 2019
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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, D. Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
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Selective Dyna-style planning under limited model capacity
Zaheer Abbas, Samuel Sokota, Erin Talvitie, and Martha White · 2020
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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 · 2020
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Deep reinforcement learning at the edge of the statistical precipice
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C Courville, and Marc Bellemare · 2021
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What matters for on-policy deep actor-critic methods? a large-scale study
Marcin Andrychowicz, Anton Raichuk, Piotr Stańczyk, Manu Orsini, Sertan Girgin, Raphaël Marinier, Leonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, and Olivier Bachem · 2021
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Model-based offline planning
Arthur Argenson and Gabriel Dulac-Arnold · 2021
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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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Ready policy one: World building through active learning
Philip Ball, Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, and Stephen Roberts · 2020
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Measuring the reliability of reinforcement learning algorithms
Stephanie C.Y. Chan, Samuel Fishman, Anoop Korattikara, John Canny, and Sergio Guadarrama · 2020
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Implementation matters in deep RL: A case study on PPO and TRPO
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, and Aleksander Madry · 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, 2020
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
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Trust the model when it is confident: Masked model-based actor-critic
Feiyang Pan, Jia He, Dandan Tu, and Qing He · 2020
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Primal Wasserstein imitation learning
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Challenges of real-world reinforcement learning: definitions, benchmarks and analysis
Gabriel Dulac-Arnold, Nir Levine, Daniel J Mankowitz, Jerry Li, Cosmin Paduraru, Sven Gowal, and Todd Hester · 2021
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Offline reinforcement learning with implicit Q-learning, 2021
Ilya Kostrikov, Ashvin Nair, and Sergey Levine · 2021
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Deployment-efficient reinforcement learning via model-based offline optimization
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Model predictive-actor critic reinforcement learning for dexterous manipulation
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Towards tractable optimism in model-based reinforcement learning
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MBRL-Lib: A modular library for model-based reinforcement learning
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Think global and act local: Bayesian optimisation over high-dimensional categorical and mixed search spaces
Xingchen Wan, Vu Nguyen, Huong Ha, Binxin Ru, Cong Lu, and Michael A. Osborne · 2021
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Behavior regularized offline reinforcement learning
Yifan Wu, George Tucker, and Ofir Nachum · 2021
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COMBO: Conservative offline model-based policy optimization
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On the importance of hyperparameter optimization for model-based reinforcement learning
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Samba: safe model-based & active reinforcement learning
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