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Diffusion planning has been recognized as an effective decision-making paradigm in various domains.
A family of embedded runge-kutta formulae
J. R. Dormand and P.J. Prince · 1980
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An Introduction to Numerical Analysis
Kendall E. Atkinson · 1989
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Reinforcement learning: An introduction
Richard S. Sutton and Andrew G. Barto · 1998
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Layer normalization
Lei Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
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Openai gym
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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A survey of deep network solutions for learning control in robotics: From reinforcement to imitation
Lei Tai, Jingwei Zhang, Ming Liu, Joschka Boedecker, and Wolfram Burgard · 2018
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Learning latent dynamics for planning from pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Model-based reinforcement learning for closed-loop dynamic control of soft robotic manipulators
Thomas George Thuruthel, Egidio Falotico, Federico Renda, and Cecilia Laschi · 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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Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning
Abhishek Gupta, Vikash Kumar, Corey Lynch, Sergey Levine, and Karol Hausman · 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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Mish: A self regularized non-monotonic activation function
Diganta Misra · 2020
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Diffusion models beat GANs on image synthesis
Prafulla Dhariwal and Alexander Quinn Nichol · 2021
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2021
Cited alongside, same era.
Variational diffusion models
Diederik P Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
Cited alongside, same era.
What matters in learning from offline human demonstrations for robot manipulation
Ajay Mandlekar, Danfei Xu, Josiah Wong, Soroush Nasiriany, Chen Wang, Rohun Kulkarni, Li Fei-Fei, Silvio Savarese, Yuke Zhu, and Roberto Martín-Martín · 2021
Cited alongside, same era.
Improved denoising diffusion probabilistic models, 2021
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
Cited alongside, same era.
Nearly horizon-free offline reinforcement learning
Tongzheng Ren, Jialian Li, Bo Dai, Simon S Du, and Sujay Sanghavi · 2021
Cited alongside, same era.
Rvs: What is essential for offline RL via supervised learning?
Scott Emmons, Benjamin Eysenbach, Ilya Kostrikov, and Sergey Levine · 2022
Diffused task-agnostic milestone planner
Mineui Hong, Minjae Kang, and Songhwai Oh · 2023
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Motiondiffuser: Controllable multi-agent motion prediction using diffusion
Chiyu Jiang, Andre Cornman, Cheolho Park, Benjamin Sapp, Yin Zhou, Dragomir Anguelov, et al · 2023
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Imagic: Text-based real image editing with diffusion models
Bahjat Kawar, Shiran Zada, Oran Lang, Omer Tov, Huiwen Chang, Tali Dekel, Inbar Mosseri, and Michal Irani · 2023
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Refining diffusion planner for reliable behavior synthesis by automatic detection of infeasible plans
Kyowoon Lee, Seongun Kim, and Jaesik Choi · 2023
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Hierarchical diffusion for offline decision making
Wenhao Li, Xiangfeng Wang, Bo Jin, and Hongyuan Zha · 2023
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Adaptdiffuser: Diffusion models as adaptive self-evolving planners
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Cited alongside, same era.
Temporal difference learning for model predictive control
Nicklas Hansen, Xiaolong Wang, and Hao Su · 2022
Cited alongside, same era.
Planning with diffusion for flexible behavior synthesis
Michael Janner, Yilun Du, Joshua B. Tenenbaum, and Sergey Levine · 2022
Cited alongside, same era.
Offline reinforcement learning with implicit q-learning
Ilya Kostrikov, Ashvin Nair, and Sergey Levine · 2022
Cited alongside, same era.
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
Cited alongside, same era.
Scalable diffusion models with transformers
William Peebles and Saining Xie · 2022
Cited alongside, same era.
NeoRL: A near real-world benchmark for offline reinforcement learning
Rong-Jun Qin, Xingyuan Zhang, Songyi Gao, Xiong-Hui Chen, Zewen Li, Weinan Zhang, and Yang Yu · 2022
Cited alongside, same era.
Zhixuan Liang, Yao Mu, Mingyu Ding, Fei Ni, Masayoshi Tomizuka, and Ping Luo · 2023
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Synthetic experience replay
Cong Lu, Philip J. Ball, and Jack Parker-Holder · 2023
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Metadiffuser: Diffusion model as conditional planner for offline meta-rl
Fei Ni, Jianye Hao, Yao Mu, Yifu Yuan, Yan Zheng, Bin Wang, and Zhixuan Liang · 2023
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Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman · 2023
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Diffusion policies as an expressive policy class for offline reinforcement learning
Zhendong Wang, Jonathan J Hunt, and Mingyuan Zhou · 2023
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Scaling robot learning with semantically imagined experience
Tianhe Yu, Ted Xiao, Austin Stone, Jonathan Tompson, Anthony Brohan, Su Wang, Jaspiar Singh, Clayton Tan, Dee M, Jodilyn Peralta, Brian Ichter, Karol Hausman, and Fei Xia · 2023
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Adding conditional control to text-to-image diffusion models
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala · 2023
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Adaptive online replanning with diffusion models
Siyuan Zhou, Yilun Du, Shun Zhang, Mengdi Xu, Yikang Shen, Wei Xiao, Dit-Yan Yeung, and Chuang Gan · 2023
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Simple hierarchical planning with diffusion
Chang Chen, Fei Deng, Kenji Kawaguchi, Caglar Gulcehre, and Sungjin Ahn · 2024
Closest in time.
Aligndiff: Aligning diverse human preferences via behavior-customisable diffusion model
Zibin Dong, Yifu Yuan, Jianye Hao, Fei Ni, Yao Mu, Yan Zheng, Yujing Hu, Tangjie Lv, Changjie Fan, and Zhipeng Hu · 2024
Closest in time.