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In many real-world settings, agents must learn from an offline dataset gathered by some prior behavior policy.
Eligibility traces for off-policy policy evaluation
Doina Precup · 2000
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Eligibility traces for off-policy policy evaluation
Doina Precup, Richard S. Sutton, and Satinder P. Singh · 2000
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Estimation of non-normalized statistical models by score matching
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Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
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Deep reinforcement learning in a handful of trials using probabilistic dynamics models, 2018
Kurtland Chua, Roberto Calandra, Rowan McAllister, and Sergey Levine · 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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D4rl: Datasets for deep data-driven reinforcement learning
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
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Jonathan Ho, Ajay Jain, and Pieter Abbeel · 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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Gaon An, Seungyong Moon, Jang-Hyun Kim, and Hyun Oh Song · 2021
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Is conditional generative modeling all you need for decision making?
Anurag Ajay, Yilun Du, Abhi Gupta, Joshua B. Tenenbaum, Tommi S. Jaakkola, and Pulkit Agrawal · 2023
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Eloi Alonso, Adam Jelley, Anssi Kanervisto, and Tim Pearce · 2023
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Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, and Tom Goldstein · 2023
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Diffusion model is an effective planner and data synthesizer for multi-task reinforcement learning
Haoran He, Chenjia Bai, Kang Xu, Zhuoran Yang, Weinan Zhang, Dong Wang, Bin Zhao, and Xuelong Li · 2023
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A survey of zero-shot generalisation in deep reinforcement learning
Robert Kirk, Amy Zhang, Edward Grefenstette, and Tim Rocktäschel · 2023
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Augmented world models facilitate zero-shot dynamics generalization from a single offline environment
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Diffusion models beat gans on image synthesis
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Scott Fujimoto and Shixiang Shane Gu · 2021
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Ilya Kostrikov, Ashvin Nair, and Sergey Levine · 2021
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Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
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Revisiting design choices in offline model based reinforcement learning
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Cong Lu, Philip J. Ball, Yee Whye Teh, and Jack Parker-Holder · 2023
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World models via policy-guided trajectory diffusion, 2023
Marc Rigter, Jun Yamada, and Ingmar Posner · 2023
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Offlinerl-kit: An elegant pytorch offline reinforcement learning library
Yihao Sun · 2023
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Diffusion policies as an expressive policy class for offline reinforcement learning, 2023
Zhendong Wang, Jonathan J Hunt, and Mingyuan Zhou · 2023
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Learning unsupervised world models for autonomous driving via discrete diffusion
Lunjun Zhang, Yuwen Xiong, Ze Yang, Sergio Casas, Rui Hu, and Raquel Urtasun · 2023
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The edge-of-reach problem in offline model-based reinforcement learning, 2024
Anya Sims, Cong Lu, and Yee Whye Teh · 2024
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