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Diffusion models have demonstrated highly-expressive generative capabilities in vision and NLP.
Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, and Sergey Levine · 1910
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ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks
Mohit Shridhar, Jesse Thomason, Daniel Gordon, Yonatan Bisk, Winson Han, Roozbeh Mottaghi, Luke Zettlemoyer, and Dieter Fox · 1912
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D4RL: datasets for deep data-driven reinforcement learning
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2004
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Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2005
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Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2006
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas 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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Lei Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
Shaojie Bai, J Zico Kolter, and Vladlen Koltun · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Quantifying generalization in reinforcement learning
Karl Cobbe, Oleg Klimov, Chris Hesse, Taehoon Kim, and John Schulman · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Multi-task reinforcement learning with soft modularization
Ruihan Yang, Huazhe Xu, Yi Wu, and Xiaolong Wang · 2020
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Gradient surgery for multi-task learning
Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn · 2020
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Varibad: A very good method for bayes-adaptive deep rl via meta-learning
Luisa Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal, Katja Hofmann, and Shimon Whiteson · 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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Bridge data: Boosting generalization of robotic skills with cross-domain datasets
Frederik Ebert, Yanlai Yang, Karl Schmeckpeper, Bernadette Bucher, Georgios Georgakis, Kostas Daniilidis, Chelsea Finn, and Sergey Levine · 2021
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A minimalist approach to offline reinforcement learning
Scott Fujimoto and Shixiang Shane Gu · 2021
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2021
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Mt-opt: Continuous multi-task robotic reinforcement learning at scale
Dmitry Kalashnikov, Jacob Varley, Yevgen Chebotar, Benjamin Swanson, Rico Jonschkowski, Chelsea Finn, Sergey Levine, and Karol Hausman · 2021
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Multi-task reinforcement learning with context-based representations
Shagun Sodhani, Amy Zhang, and Joelle Pineau · 2021
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Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
Denis Yarats, Ilya Kostrikov, and Rob Fergus · 2021
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Conservative data sharing for multi-task offline reinforcement learning
Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Sergey Levine, and Chelsea Finn · 2021
Cited alongside, same era.
Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V Le · 2022
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Ying Wen, Ziyu Wan, Ming Zhou, Shufang Hou, Zhe Cao, Chenyang Le, Jingxiao Chen, Zheng Tian, Weinan Zhang, and Jun Wang · 2022
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Tackling the generative learning trilemma with denoising diffusion gans
Zhisheng Xiao, Karsten Kreis, and Arash Vahdat · 2022
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Robust task representations for offline meta-reinforcement learning via contrastive learning
Haoqi Yuan and Zongqing Lu · 2022
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Generative planning for temporally coordinated exploration in reinforcement learning
Haichao Zhang, Wei Xu, and Haonan Yu · 2022
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Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, et al · 2022
Cited alongside, same era.
Rt-1: Robotics transformer for real-world control at scale
Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Jasmine Hsu, et al · 2022
Cited alongside, same era.
Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al · 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.
Multi-game decision transformers
Kuang-Huei Lee, Ofir Nachum, Mengjiao Sherry Yang, Lisa Lee, Daniel Freeman, Sergio Guadarrama, Ian Fischer, Winnie Xu, Eric Jang, Henryk Michalewski, et al · 2022
Cited alongside, same era.
Diffusion-LM improves controllable text generation
Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, and Tatsunori Hashimoto · 2022
Cited alongside, same era.
Is conditional generative modeling all you need for decision making?
Anurag Ajay, Yilun Du, Abhi Gupta, Joshua B. Tenenbaum, Tommi S. Jaakkola, and Pulkit Agrawal · 2023
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One transformer fits all distributions in multi-modal diffusion at scale
Fan Bao, Shen Nie, Kaiwen Xue, Chongxuan Li, Shi Pu, Yaole Wang, Gang Yue, Yue Cao, Hang Su, and Jun Zhu · 2023
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Diffusion policy: Visuomotor policy learning via action diffusion
Cheng Chi, Siyuan Feng, Yilun Du, Zhenjia Xu, Eric Cousineau, Benjamin Burchfiel, and Shuran Song · 2023
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Diffuseq: Sequence to sequence text generation with diffusion models
Shansan Gong, Mukai Li, Jiangtao Feng, Zhiyong Wu, and Lingpeng Kong · 2023
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Offline q-learning on diverse multi-task data both scales and generalizes
Aviral Kumar, Rishabh Agarwal, Xinyang Geng, George Tucker, and Sergey Levine · 2023
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From distribution learning in training to gradient search in testing for combinatorial optimization
Yang Li, Jinpei Guo, Runzhong Wang, and Junchi Yan · 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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Synthetic experience replay
Cong Lu, Philip J. Ball, and Jack Parker-Holder · 2023
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Gpt-4 technical report, 2023
OpenAI · 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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Consistency models, 2023
Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever · 2023
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SMART: Self-supervised multi-task pretraining with control transformers
Yanchao Sun, Shuang Ma, Ratnesh Madaan, Rogerio Bonatti, Furong Huang, and Ashish Kapoor · 2023
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Investigating multi-task pretraining and generalization in reinforcement learning
Adrien Ali Taiga, Rishabh Agarwal, Jesse Farebrother, Aaron Courville, and Marc G Bellemare · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 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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Visual chatgpt: Talking, drawing and editing with visual foundation models
Chenfei Wu, Shengming Yin, Weizhen Qi, Xiaodong Wang, Zecheng Tang, and Nan Duan · 2023
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Using both demonstrations and language instructions to efficiently learn robotic tasks
Albert Yu and Ray Mooney · 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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