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Diffusion models surpass previous generative models in sample quality and training stability.
Mcmc using hamiltonian dynamics
Radford M Neal et al · 2011
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Reinforcement learning in robotics: A survey
Jens Kober, J Andrew Bagnell, and Jan Peters · 2013
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Constrained policy optimization
Joshua Achiam, David Held, Aviv Tamar, and Pieter Abbeel · 2017
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Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning
Anusha Nagabandi, Gregory Kahn, Ronald S Fearing, 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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Reinforcement learning upside down: Don’t predict rewards–just map them to actions
Juergen Schmidhuber · 2019
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Learning to combat compounding-error in model-based reinforcement learning
Chenjun Xiao, Yifan Wu, Chen Ma, Dale Schuurmans, and Martin Müller · 2019
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Denoising diffusion probabilistic models, 2020
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Diffwave: A versatile diffusion model for audio synthesis
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro · 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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Reinforcement learning with augmented data
Misha Laskin, Kimin Lee, Adam Stooke, Lerrel Pinto, Pieter Abbeel, and Aravind Srinivas · 2020
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User behavior retrieval for click-through rate prediction
Jiarui Qin, Weinan Zhang, Xin Wu, Jiarui Jin, Yuchen Fang, and Yong Yu · 2020
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Mastering atari, go, chess and shogi by planning with a learned model
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, et al · 2020
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A survey of multi-task deep reinforcement learning
Nelson Vithayathil Varghese and Qusay H Mahmoud · 2020
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Structured denoising diffusion models in discrete state-spaces
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne Van Den Berg · 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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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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A minimalist approach to offline reinforcement learning
Scott Fujimoto and Shixiang Shane Gu · 2021
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An investigation of generative replay in deep reinforcement learning
Baris Imre · 2021
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Deep reinforcement learning for autonomous driving: A survey
B Ravi Kiran, Ibrahim Sobh, Victor Talpaert, Patrick Mannion, Ahmad A Al Sallab, Senthil Yogamani, and Patrick Pérez · 2021
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Offline reinforcement learning with fisher divergence critic regularization
Ilya Kostrikov, Rob Fergus, Jonathan Tompson, and Ofir Nachum · 2021
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Nu-wave: A diffusion probabilistic model for neural audio upsampling
Junhyeok Lee and Seungu Han · 2021
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Conflict-averse gradient descent for multi-task learning
Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, and Qiang Liu · 2021
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Diffusion probabilistic models for 3d point cloud generation
Shitong Luo and Wei Hu · 2021
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S4rl: Surprisingly simple self-supervision for offline reinforcement learning, 2021
Samarth Sinha, Ajay Mandlekar, and Animesh Garg · 2021
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Score-based generative modeling through stochastic differential equations, 2021
Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
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Learning to efficiently sample from diffusion probabilistic models, 2021
Daniel Watson, Jonathan Ho, Mohammad Norouzi, and William Chan · 2021
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Mapgo: Model-assisted policy optimization for goal-oriented tasks
Menghui Zhu, Minghuan Liu, Jian Shen, Zhicheng Zhang, Sheng Chen, Weinan Zhang, Deheng Ye, Yong Yu, Qiang Fu, and Wei Yang · 2021
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Executing your commands via motion diffusion in latent space
Xin Chen, Biao Jiang, Wen Liu, Zilong Huang, Bin Fu, Tao Chen, Jingyi Yu, and Gang Yu · 2022
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S2p: State-conditioned image synthesis for data augmentation in offline reinforcement learning
Daesol Cho, Dongseok Shim, and H Jin Kim · 2022
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Mofusion: A framework for denoising-diffusion-based motion synthesis
Rishabh Dabral, Muhammad Hamza Mughal, Vladislav Golyanik, and Christian Theobalt · 2022
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Classifier-free diffusion guidance, 2022
Jonathan Ho and Tim Salimans · 2022
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Planning with diffusion for flexible behavior synthesis
Michael Janner, Yilun Du, Joshua B. Tenenbaum, and Sergey Levine · 2022
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Dall-e-bot: Introducing web-scale diffusion models to robotics
Ivan Kapelyukh, Vitalis Vosylius, and Edward Johns · 2022
Cited alongside, same era.
Diffusion-lm improves controllable text generation
Xiang Li, John Thickstun, Ishaan Gulrajani, Percy S Liang, and Tatsunori B Hashimoto · 2022
Cited alongside, same era.
Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps, 2022
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu · 2022
Cited alongside, same era.
Repaint: Inpainting using denoising diffusion probabilistic models
Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool · 2022
Cited alongside, same era.
A survey on model-based reinforcement learning
Fan-Ming Luo, Tian Xu, Hang Lai, Xiong-Hui Chen, Weinan Zhang, and Yang Yu · 2022
Cited alongside, same era.
Motiondiffuser: Controllable multi-agent motion prediction using diffusion, 2023
Chiyu Max Jiang, Andre Cornman, Cheolho Park, Ben Sapp, Yin Zhou, and Dragomir Anguelov · 2023
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Large language models struggle to learn long-tail knowledge
Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, and Colin Raffel · 2023
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Efficient diffusion policies for offline reinforcement learning, 2023
Bingyi Kang, Xiao Ma, Chao Du, Tianyu Pang, and Shuicheng Yan · 2023
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Gen2sim: Scaling up robot learning in simulation with generative models
Pushkal Katara, Zhou Xian, and Katerina Fragkiadaki · 2023
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Learning to act from actionless videos through dense correspondences, 2023
Po-Chen Ko, Jiayuan Mao, Yilun Du, Shao-Hua Sun, and Joshua B. Tenenbaum · 2023
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Mildly conservative q-learning for offline reinforcement learning
Jiafei Lyu, Xiaoteng Ma, Xiu Li, and Zongqing Lu · 2022
Cited alongside, same era.
Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, et al · 2022
Cited alongside, same era.
Structure-based drug design with dvariant diffusion models
Arne Schneuing, Yuanqi Du, Charles Harris, Arian Jamasb, Ilia Igashov, Weitao Du, Tom Blundell, Pietro Lió, Carla Gomes, Max Welling, et al · 2022
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Knn-diffusion: Image generation via large-scale retrieval
Shelly Sheynin, Oron Ashual, Adam Polyak, Uriel Singer, Oran Gafni, Eliya Nachmani, and Yaniv Taigman · 2022
Cited alongside, same era.
Denoising diffusion implicit models, 2022
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2022
Cited alongside, same era.
Investigating multi-task pretraining and generalization in reinforcement learning
Adrien Ali Taiga, Rishabh Agarwal, Jesse Farebrother, Aaron Courville, and Marc G Bellemare · 2022
Cited alongside, same era.
Human motion diffusion model, 2022
Guy Tevet, Sigal Raab, Brian Gordon, Yonatan Shafir, Daniel Cohen-Or, and Amit H. Bermano · 2022
Cited alongside, same era.
Wenhao Li, Xiangfeng Wang, Bo Jin, and Hongyuan Zha · 2023
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Crossway diffusion: Improving diffusion-based visuomotor policy via self-supervised learning, 2023
Xiang Li, Varun Belagali, Jinghuan Shang, and Michael S. Ryoo · 2023
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Beyond conservatism: Diffusion policies in offline multi-agent reinforcement learning, 2023
Zhuoran Li, Ling Pan, and Longbo Huang · 2023
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Efficient planning with latent diffusion, 2023
Wenhao 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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Zhixuan Liang, Yao Mu, Hengbo Ma, Masayoshi Tomizuka, Mingyu Ding, and Ping Luo · 2023
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Constrained decision transformer for offline safe reinforcement learning
Zuxin Liu, Zijian Guo, Yihang Yao, Zhepeng Cen, Wenhao Yu, Tingnan Zhang, and Ding Zhao · 2023
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Contrastive energy prediction for exact energy-guided diffusion sampling in offline reinforcement learning, 2023
Cheng Lu, Huayu Chen, Jianfei Chen, Hang Su, Chongxuan Li, and Jun Zhu · 2023
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Synthetic experience replay
Cong Lu, Philip J. Ball, and Jack Parker-Holder · 2023
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Value function estimation using conditional diffusion models for control, 2023
Bogdan Mazoure, Walter Talbott, Miguel Angel Bautista, Devon Hjelm, Alexander Toshev, and Josh Susskind · 2023
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Generative skill chaining: Long-horizon skill planning with diffusion models
Utkarsh Aashu Mishra, Shangjie Xue, Yongxin Chen, and Danfei Xu · 2023
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Diffusion co-policy for synergistic human-robot collaborative tasks, 2023
Eley Ng, Ziang Liu, and Monroe Kennedy III au2 · 2023
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Metadiffuser: Diffusion model as conditional planner for offline meta-rl, 2023
Fei Ni, Jianye Hao, Yao Mu, Yifu Yuan, Yan Zheng, Bin Wang, and Zhixuan Liang · 2023
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Imitating human behaviour with diffusion models
Tim Pearce, Tabish Rashid, Anssi Kanervisto, Dave Bignell, Mingfei Sun, Raluca Georgescu, Sergio Valcarcel Macua, Shan Zheng Tan, Ida Momennejad, Katja Hofmann, and Sam Devlin · 2023
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Single motion diffusion, 2023
Sigal Raab, Inbal Leibovitch, Guy Tevet, Moab Arar, Amit H. Bermano, and Daniel Cohen-Or · 2023
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Goal-conditioned imitation learning using score-based diffusion policies, 2023
Moritz Reuss, Maximilian Li, Xiaogang Jia, and Rudolf Lioutikov · 2023
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World models via policy-guided trajectory diffusion
Marc Rigter, Jun Yamada, and Ingmar Posner · 2023
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Nomad: Goal masked diffusion policies for navigation and exploration, 2023
Ajay Sridhar, Dhruv Shah, Catherine Glossop, and Sergey Levine · 2023
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Reasoning with latent diffusion in offline reinforcement learning, 2023
Siddarth Venkatraman, Shivesh Khaitan, Ravi Tej Akella, John Dolan, Jeff Schneider, and Glen Berseth · 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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Chaineddiffuser: Unifying trajectory diffusion and keypose prediction for robotic manipulation
Zhou Xian, Nikolaos Gkanatsios, Theophile Gervet, Tsung-Wei Ke, and Katerina Fragkiadaki · 2023
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Safediffuser: Safe planning with diffusion probabilistic models, 2023
Wei Xiao, Tsun-Hsuan Wang, Chuang Gan, and Daniela Rus · 2023
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Xskill: Cross embodiment skill discovery, 2023
Mengda Xu, Zhenjia Xu, Cheng Chi, Manuela Veloso, and Shuran Song · 2023
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Policy representation via diffusion probability model for reinforcement learning
Long Yang, Zhixiong Huang, Fenghao Lei, Yucun Zhong, Yiming Yang, Cong Fang, Shiting Wen, Binbin Zhou, and Zhouchen Lin · 2023
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Learning interactive real-world simulators, 2023
Mengjiao Yang, Yilun Du, Kamyar Ghasemipour, Jonathan Tompson, Dale Schuurmans, and Pieter Abbeel · 2023
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To the noise and back: Diffusion for shared autonomy, 2023
Takuma Yoneda, Luzhe Sun, , Ge Yang, Bradly Stadie, and Matthew Walter · 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, Jodilyn Peralta, Brian Ichter, et al · 2023
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Lad: Language control diffusion: efficiently scaling through space, time, and tasks
Edwin Zhang, Yujie Lu, William Wang, and Amy Zhang · 2023
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Remodiffuse: Retrieval-augmented motion diffusion model, 2023
Mingyuan Zhang, Xinying Guo, Liang Pan, Zhongang Cai, Fangzhou Hong, Huirong Li, Lei Yang, and Ziwei Liu · 2023
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Truncated diffusion probabilistic models and diffusion-based adversarial auto-encoders, 2023
Huangjie Zheng, Pengcheng He, Weizhu Chen, and Mingyuan Zhou · 2023
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Madiff: Offline multi-agent learning with diffusion models, 2023
Zhengbang Zhu, Minghuan Liu, Liyuan Mao, Bingyi Kang, Minkai Xu, Yong Yu, Stefano Ermon, and Weinan Zhang · 2023
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