Fetching the paper…
Reading the bibliography…
Leveraging the powerful generative capability of diffusion models (DMs) to build decision-making agents has achieved extensive success.
Numerical Solution of Stochastic Differential Equations
P.E. Kloeden and E. Platen · 2011
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
Earlier work this paper cites.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Earlier work this paper cites.
Generative adversarial networks: An overview
Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, and Anil A Bharath · 2018
Earlier work this paper cites.
Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke Hoof, and David Meger · 2018
Earlier work this paper cites.
RLlib: Abstractions for distributed reinforcement learning
Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph Gonzalez, Michael Jordan, and Ion Stoica · 2018
Earlier work this paper cites.
Film: Visual reasoning with a general conditioning layer
Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville · 2018
Earlier work this paper cites.
Learning latent dynamics for planning from pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2019
Earlier work this paper cites.
Hydra - a framework for elegantly configuring complex applications
Omry Yadan · 2019
Earlier work this paper cites.
D4rl: Datasets for deep data-driven reinforcement learning
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
Earlier work this paper cites.
Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning
Abhishek Gupta, Vikash Kumar, Corey Lynch, Sergey Levine, and Karol Hausman · 2020
Earlier work this paper cites.
Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi · 2020
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Earlier work this paper cites.
Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2020
Earlier work this paper cites.
Learning to generalize across long-horizon tasks from human demonstrations
Ajay Mandlekar, Danfei Xu, Roberto Martín-Martín, Silvio Savarese, and Li Fei-Fei · 2020
Earlier work this paper cites.
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
Earlier work this paper cites.
Zarr-python
Zarr Contributors · 2021
Earlier work this paper cites.
Diffusion models beat GANs on image synthesis
Prafulla Dhariwal and Alexander Quinn Nichol · 2021
Earlier work this paper cites.
A minimalist approach to offline reinforcement learning
Scott Fujimoto and Shixiang Gu · 2021
Earlier work this paper cites.
Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2021
Earlier work this paper cites.
An investigation of generative replay in deep reinforcement learning, January 2021
Baris Imre · 2021
Earlier work this paper cites.
Variational diffusion models
Diederik P Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
Earlier work this paper cites.
Hyar: Addressing discrete-continuous action reinforcement learning via hybrid action representation
Boyan Li, Hongyao Tang, Yan Zheng, Jianye Hao, Pengyi Li, Zhen Wang, Zhaopeng Meng, and Li Wang · 2021
Earlier work this paper cites.
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
Earlier work this paper cites.
Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
Cited alongside, same era.
Stable-baselines3: Reliable reinforcement learning implementations
Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, and Noah Dormann · 2021
Cited alongside, same era.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
Cited alongside, same era.
S2p: state-conditioned image synthesis for data augmentation in offline reinforcement learning
Daesol Clio, Dongseok Shim, and H. Jin Kim · 2022
Cited alongside, same era.
Contrastive energy prediction for exact energy-guided diffusion sampling in offline reinforcement learning
Cheng Lu, Huayu Chen, Jianfei Chen, Hang Su, Chongxuan Li, and Jun Zhu · 2023
Later among the works it cites.
Dpm-solver++: Fast solver for guided sampling of diffusion probabilistic models
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu · 2023
Later among the works it cites.
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
Later among the works it cites.
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
Later among the works it cites.
Scalable diffusion models with transformers
William Peebles and Saining Xie · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Sander Dieleman, Laurent Sartran, Arman Roshannai, Nikolay Savinov, Yaroslav Ganin, Pierre H Richemond, Arnaud Doucet, Robin Strudel, Chris Dyer, Conor Durkan, et al · 2022
Cited alongside, same era.
Implicit behavioral cloning
Pete Florence, Corey Lynch, Andy Zeng, Oscar A Ramirez, Ayzaan Wahid, Laura Downs, Adrian Wong, Johnny Lee, Igor Mordatch, and Jonathan Tompson · 2022
Cited alongside, same era.
Temporal difference learning for model predictive control
Nicklas A Hansen, Hao Su, and Xiaolong Wang · 2022
Cited alongside, same era.
Planning with diffusion for flexible behavior synthesis
Michael Janner, Yilun Du, Joshua Tenenbaum, and Sergey Levine · 2022
Cited alongside, same era.
Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 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.
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
Later among the works it cites.
Diffusion policies as an expressive policy class for offline reinforcement learning
Zhendong Wang, Jonathan J Hunt, and Mingyuan Zhou · 2023
Later among the works it cites.
Chaineddiffuser: Unifying trajectory diffusion and keypose prediction for robotic manipulation
Zhou Xian, Nikolaos Gkanatsios, Theophile Gervet, Tsung-Wei Ke, and Katerina Fragkiadaki · 2023
Later among the works it cites.
Adding conditional control to text-to-image diffusion models
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala · 2023
Later among the works it cites.
Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
Tony Z. Zhao, Vikash Kumar, Sergey Levine, and Chelsea Finn · 2023
Later among the works it cites.
Diffusion models for reinforcement learning: A survey
Zhengbang Zhu, Hanye Zhao, Haoran He, Yichao Zhong, Shenyu Zhang, Yong Yu, and Weinan Zhang · 2023
Later among the works it cites.
Diffuserlite: Towards real-time diffusion planning
Zibin Dong, Jianye Hao, Yifu Yuan, Fei Ni, Yitian Wang, Pengyi Li, and Yan Zheng · 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.
TD-MPC2: Scalable, robust world models for continuous control
Nicklas Hansen, Hao Su, and Xiaolong Wang · 2024
Closest in time.
Multiagent gumbel muzero: Efficient planning in combinatorial action spaces
Xiaotian Hao, Jianye Hao, Chenjun Xiao, Kai Li, Dong Li, and Yan Zheng · 2024
Closest in time.
Diffcps: Diffusion model based constrained policy search for offline reinforcement learning
Longxiang He, Li Shen, Linrui Zhang, Junbo Tan, and Xueqian Wang · 2024
Closest in time.
Efficient diffusion policies for offline reinforcement learning
Bingyi Kang, Xiao Ma, Chao Du, Tianyu Pang, and Shuicheng Yan · 2024
Closest in time.
Synthetic experience replay
Cong Lu, Philip Ball, Yee Whye Teh, and Jack Parker-Holder · 2024
Closest in time.
The blessing of randomness: SDE beats ODE in general diffusion-based image editing
Shen Nie, Hanzhong Allan Guo, Cheng Lu, Yuhao Zhou, Chenyu Zheng, and Chongxuan Li · 2024
Closest in time.
Octo: An open-source generalist robot policy
Octo Model Team, Dibya Ghosh, Homer Walke, Karl Pertsch, Kevin Black, Oier Mees, Sudeep Dasari, Joey Hejna, Charles Xu, Jianlan Luo, Tobias Kreiman, You Liang Tan, Lawrence Yunliang Chen, Pannag Sanketi, Quan Vuong, Ted Xiao, Dorsa Sadigh, Chelsea Finn, and Sergey Levine · 2024
Closest in time.
Learning interactive real-world simulators
Sherry Yang, Yilun Du, Seyed Kamyar Seyed Ghasemipour, Jonathan Tompson, Leslie Pack Kaelbling, Dale Schuurmans, and Pieter Abbeel · 2024
Closest in time.
3d diffusion policy: Generalizable visuomotor policy learning via simple 3d representations
Yanjie Ze, Gu Zhang, Kangning Zhang, Chenyuan Hu, Muhan Wang, and Huazhe Xu · 2024
Closest in time.
Language control diffusion: Efficiently scaling through space, time, and tasks
Edwin Zhang, Yujie Lu, Shinda Huang, William Yang Wang, and Amy Zhang · 2024
Closest in time.