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Offline Reinforcement Learning (Offline RL) presents challenges of learning effective decision-making policies from static datasets without any online interactions.
The generalization of ‘student’s’problem when several different population varlances are involved
Bernard L Welch · 1947
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Batch reinforcement learning
Sascha Lange, Thomas Gabel, and Martin Riedmiller · 2012
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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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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Behavior regularized offline reinforcement learning
Yifan Wu, George Tucker, and Ofir Nachum · 2019
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Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 2019
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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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Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2020
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Awac: Accelerating online reinforcement learning with offline datasets
Ashvin Nair, Abhishek Gupta, Murtaza Dalal, and Sergey Levine · 2020
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Reinforcement learning with augmented data
Misha Laskin, Kimin Lee, Adam Stooke, Lerrel Pinto, Pieter Abbeel, and Aravind Srinivas · 2020
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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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Curl: Contrastive unsupervised representations for reinforcement learning
Michael Laskin, Aravind Srinivas, and Pieter Abbeel · 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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Morel: Model-based offline reinforcement learning
Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, and Thorsten Joachims · 2020
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
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Model inversion networks for model-based optimization
Aviral Kumar and Sergey Levine · 2020
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A minimalist approach to offline reinforcement learning
Scott Fujimoto and Shixiang Shane Gu · 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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Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
Denis Yarats, Rob Fergus, and Ilya Kostrikov · 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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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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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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Rambo-rl: Robust adversarial model-based offline reinforcement learning
Marc Rigter, Bruno Lacerda, and Nick Hawes · 2022
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Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
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Corl: Research-oriented deep offline reinforcement learning library
Denis Tarasov, Alexander Nikulin, Dmitry Akimov, Vladislav Kurenkov, and Sergey Kolesnikov · 2022
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Biological sequence design with gflownets
Moksh Jain, Emmanuel Bengio, Alex Hernandez-Garcia, Jarrid Rector-Brooks, Bonaventure FP Dossou, Chanakya Ajit Ekbote, Jie Fu, Tianyu Zhang, Michael Kilgour, Dinghuai Zhang, et al · 2022
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Mlp-mixer: An all-mlp architecture for vision
Ilya O Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, et al · 2021
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Offline reinforcement learning with implicit q-learning
Ilya Kostrikov, Ashvin Nair, and Sergey Levine · 2021
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Decision transformer: Reinforcement learning via sequence modeling
Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Misha Laskin, Pieter Abbeel, Aravind Srinivas, and Igor Mordatch · 2021
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Revisiting design choices in offline model-based reinforcement learning
Cong Lu, Philip J Ball, Jack Parker-Holder, Michael A Osborne, and Stephen J Roberts · 2021
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Sdedit: Guided image synthesis and editing with stochastic differential equations
Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon · 2021
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Mildly conservative q-learning for offline reinforcement learning
Jiafei Lyu, Xiaoteng Ma, Xiu Li, and Zongqing Lu · 2022
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Bootstrapped transformer for offline reinforcement learning
Kerong Wang, Hanye Zhao, Xufang Luo, Kan Ren, Weinan Zhang, and Dongsheng Li · 2022
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Antigen-specific antibody design and optimization with diffusion-based generative models for protein structures
Shitong Luo, Yufeng Su, Xingang Peng, Sheng Wang, Jian Peng, and Jianzhu Ma · 2022
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Cong Lu, Philip J Ball, and Jack Parker-Holder · 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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Adaptdiffuser: Diffusion models as adaptive self-evolving planners
Zhixuan Liang, Yao Mu, Mingyu Ding, Fei Ni, Masayoshi Tomizuka, and Ping Luo · 2023
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Diffusion models for black-box optimization
Siddarth Krishnamoorthy, Satvik Mehul Mashkaria, and Aditya Grover · 2023
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Bootstrapped training of score-conditioned generator for offline design of biological sequences
Minsu Kim, Federico Berto, Sungsoo Ahn, and Jinkyoo Park · 2023
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Offlinerl-kit: An elegant pytorch offline reinforcement learning library
Yihao Sun · 2023
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Harnessing mixed offline reinforcement learning datasets via trajectory weighting
Zhang-Wei Hong, Pulkit Agrawal, Rémi Tachet des Combes, and Romain Laroche · 2023
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Effective data augmentation with diffusion models
Brandon Trabucco, Kyle Doherty, Max Gurinas, and Ruslan Salakhutdinov · 2023
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Matthew Thomas Jackson, Michael Tryfan Matthews, Cong Lu, Benjamin Ellis, Shimon Whiteson, and Jakob Foerster · 2024
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