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Offline reinforcement learning (RL) leverages pre-collected datasets to train optimal policies.
Double Q-learning
Hado Hasselt · 2010
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2010
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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 · 2011
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Batch reinforcement learning
Sascha Lange, Thomas Gabel, and Martin Riedmiller · 2012
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Reinforcement learning
Marco A Wiering and Martijn Van Otterlo · 2012
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Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 2013
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Energy-based generative adversarial network
Junbo Zhao, Michael Mathieu, and Yann LeCun · 2016
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A distributional perspective on reinforcement learning
Marc G Bellemare, Will Dabney, and Rémi Munos · 2017
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Deep reinforcement learning: An overview
Yuxi Li · 2017
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Distributed distributional deterministic policy gradients
Gabriel Barth-Maron, Matthew W Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva Tb, Alistair Muldal, Nicolas Heess, and Timothy Lillicrap · 2018
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Do deep generative models know what they don’t know?
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, and Balaji Lakshminarayanan · 2018
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Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 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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Behavior regularized offline reinforcement learning
Yifan Wu, George Tucker, and Ofir Nachum · 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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Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 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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Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
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DPM-Solver: A fast ODE solver for diffusion probabilistic model sampling in around 10 steps
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu · 2022
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DreamFusion: Text-to-3D using 2D diffusion
Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall · 2022
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A behavior regularized implicit policy for offline reinforcement learning
Shentao Yang, Zhendong Wang, Huangjie Zheng, Yihao Feng, and Mingyuan Zhou · 2022
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Score regularized policy optimization through diffusion behavior
Huayu Chen, Cheng Lu, Zhengyi Wang, Hang Su, and Jun Zhu · 2023
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Implicit distributional reinforcement learning
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Variational diffusion models
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Offline reinforcement learning with implicit Q-learning
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Maximum likelihood training of score-based diffusion models
Yang Song, Conor Durkan, Iain Murray, and Stefano Ermon · 2021
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Offline reinforcement learning via high-fidelity generative behavior modeling
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IDQL: Implicit Q-learning as an actor-critic method with diffusion policies
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Imitating human behaviour with diffusion models
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One-step diffusion with distribution matching distillation
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Efficient diffusion policies for offline reinforcement learning
Bingyi Kang, Xiao Ma, Chao Du, Tianyu Pang, and Shuicheng Yan · 2024
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Understanding diffusion objectives as the ELBO with simple data augmentation
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Diff-Instruct: A universal approach for transferring knowledge from pre-trained diffusion models
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ProlificDreamer: High-fidelity and diverse text-to-3D generation with variational score distillation
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Score identity distillation: Exponentially fast distillation of pretrained diffusion models for one-step generation
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