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Diffusion-based generative methods have proven effective in modeling trajectories with offline datasets.
Planning in a hierarchy of abstraction spaces
Earl D Sacerdoti · 1974
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Learning abstraction hierarchies for problem solving
Craig A Knoblock · 1990
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Reinforcement learning with a hierarchy of abstract models
Satinder P Singh · 1992
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Pilco: A model-based and data-efficient approach to policy search
Marc Deisenroth and Carl E Rasmussen · 2011
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Model regularization for stable sample rollouts
Erik Talvitie · 2014
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U-Net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. 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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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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Aggressive driving with model predictive path integral control
Grady Williams, Paul Drews, Brian Goldfain, James M Rehg, and Evangelos A Theodorou · 2016
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Hierarchical multiscale recurrent neural networks
Junyoung Chung, Sungjin Ahn, and Yoshua Bengio · 2017
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Sample-efficient reinforcement learning with stochastic ensemble value expansion
Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, and Honglak Lee · 2018
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David Ha and Jürgen Schmidhuber · 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 · 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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Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi · 2019
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Variational temporal abstraction
Taesup Kim, Sungjin Ahn, and Yoshua Bengio · 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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Why does hierarchy (sometimes) work so well in reinforcement learning?
Ofir Nachum, Haoran Tang, Xingyu Lu, Shixiang Gu, Honglak Lee, 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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On the role of planning in model-based deep reinforcement learning
Jessica B Hamrick, Abram L Friesen, Feryal Behbahani, Arthur Guez, Fabio Viola, Sims Witherspoon, Thomas Anthony, Lars Buesing, Petar Veličković, and Théophane Weber · 2020
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Denoising diffusion probabilistic models
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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Iris: Implicit reinforcement without interaction at scale for learning control from offline robot manipulation data
Ajay Mandlekar, Fabio Ramos, Byron Boots, Silvio Savarese, Li Fei-Fei, Animesh Garg, and Dieter Fox · 2020
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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Robustness implies generalization via data-dependent generalization bounds
Kenji Kawaguchi, Zhun Deng, Kyle Luh, and Jiaoyang Huang · 2022
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Offline reinforcement learning with implicit Q-learning
Ilya Kostrikov, Ashvin Nair, and Sergey Levine · 2022
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Hierarchical planning through goal-conditioned offline reinforcement learning
Jinning Li, Chen Tang, Masayoshi Tomizuka, and Wei Zhan · 2022
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Imitating human behaviour with diffusion models
Tim Pearce, Tabish Rashid, Anssi Kanervisto, David Bignell, Mingfei Sun, Raluca Georgescu, Sergio Valcarcel Macua, Shan Zheng Tan, Ida Momennejad, Katja Hofmann, and Sam Devlin · 2022
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Long-horizon visual planning with goal-conditioned hierarchical predictors
Karl Pertsch, Oleh Rybkin, Frederik Ebert, Shenghao Zhou, Dinesh Jayaraman, Chelsea Finn, and Sergey Levine · 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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Exploring model-based planning with policy networks
Tingwu Wang and Jimmy Ba · 2020
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Latent skill planning for exploration and transfer
Kevin Xie, Homanga Bharadhwaj, Danijar Hafner, Animesh Garg, and Florian Shkurti · 2020
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Decision transformer: Reinforcement learning via sequence modeling
Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael 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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Offline reinforcement learning as one big sequence modeling problem
Michael Janner, Qiyang Li, and Sergey Levine · 2021
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Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al · 2022
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Skill-based model-based reinforcement learning
Lucy Xiaoyang Shi, Joseph J. Lim, and Youngwoon Lee · 2022
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Diffusion policies as an expressive policy class for offline reinforcement learning
Zhendong Wang, Jonathan J Hunt, and Mingyuan Zhou · 2022
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EDGI: Equivariant diffusion for planning with embodied agents
Johann Brehmer, Joey Bose, Pim De Haan, and Taco Cohen · 2023
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Offline reinforcement learning via high-fidelity generative behavior modeling
Huayu Chen, Cheng Lu, Chengyang Ying, Hang Su, and Jun Zhu · 2023
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Simplifying model-based RL: Learning representations, latent-space models, and policies with one objective
Raj Ghugare, Homanga Bharadhwaj, Benjamin Eysenbach, Sergey Levine, and Russ Salakhutdinov · 2023
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Planning goals for exploration
Edward S. Hu, Richard Chang, Oleh Rybkin, and Dinesh Jayaraman · 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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Cheng Lu, Huayu Chen, Jianfei Chen, Hang Su, Chongxuan Li, and Jun Zhu · 2023
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Model-based reinforcement learning: A survey
Thomas M Moerland, Joost Broekens, Aske Plaat, Catholijn M Jonker, et al · 2023
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Mastering the unsupervised reinforcement learning benchmark from pixels
Sai Rajeswar, Pietro Mazzaglia, Tim Verbelen, Alexandre Piché, Bart Dhoedt, Aaron Courville, and Alexandre Lacoste · 2023
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Leveraging jumpy models for planning and fast learning in robotic domains
Jingwei Zhang, Jost Tobias Springenberg, Arunkumar Byravan, Leonard Hasenclever, Abbas Abdolmaleki, Dushyant Rao, Nicolas Heess, and Martin Riedmiller · 2023
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Making better decision by directly planning in continuous control
Jinhua Zhu, Yue Wang, Lijun Wu, Tao Qin, Wengang Zhou, Tie-Yan Liu, and Houqiang Li · 2023
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