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Offline reinforcement learning (RL) presents an attractive paradigm for training intelligent agents without expensive online interactions.
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Gaussian error linear units (gelus)
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Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation
Tejas D Kulkarni, Karthik Narasimhan, Ardavan Saeedi, and Josh Tenenbaum · 2016
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The option-critic architecture
Pierre-Luc Bacon, Jean Harb, and Doina Precup · 2017
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Data-efficient hierarchical reinforcement learning
Ofir Nachum, Shixiang Shane Gu, Honglak Lee, and Sergey Levine · 2018
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Self-imitation learning
Junhyuk Oh, Yijie Guo, Satinder Singh, and Honglak Lee · 2018
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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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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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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Advantage-weighted regression: Simple and scalable off-policy reinforcement learning
Xue Bin Peng, Aviral Kumar, Grace Zhang, 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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Rl unplugged: A suite of benchmarks for offline reinforcement learning
Caglar Gulcehre, Ziyu Wang, Alexander Novikov, Thomas Paine, Sergio Gómez, Konrad Zolna, Rishabh Agarwal, Josh S Merel, Daniel J Mankowitz, Cosmin Paduraru, et al · 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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Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Offline RL with no OOD actions: In-sample learning via implicit value regularization
Haoran Xu, Li Jiang, Jianxiong Li, Zhuoran Yang, Zhaoran Wang, Victor Wai Kin Chan, and Xianyuan Zhan · 2023
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Learning fine-grained bimanual manipulation with low-cost hardware
Tony Z Zhao, Vikash Kumar, Sergey Levine, and Chelsea Finn · 2023
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Stop regressing: Training value functions via classification for scalable deep RL
Jesse Farebrother, Jordi Orbay, Quan Vuong, Adrien Ali Taiga, Yevgen Chebotar, Ted Xiao, Alex Irpan, Sergey Levine, Pablo Samuel Castro, Aleksandra Faust, Aviral Kumar, and Rishabh Agarwal · 2024
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Top-erl: Transformer-based off-policy episodic reinforcement learning
Ge Li, Dong Tian, Hongyi Zhou, Xinkai Jiang, Rudolf Lioutikov, and Gerhard Neumann · 2024
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Steering your generalists: Improving robotic foundation models via value guidance
Mitsuhiko Nakamoto, Oier Mees, Aviral Kumar, and Sergey Levine · 2024
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Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
Cited alongside, same era.
Awac: Accelerating online reinforcement learning with offline datasets
Ashvin Nair, Abhishek Gupta, Murtaza Dalal, and Sergey Levine · 2020
Cited alongside, same era.
Critic regularized regression
Ziyu Wang, Alexander Novikov, Konrad Zolna, Josh S Merel, Jost Tobias Springenberg, Scott E Reed, Bobak Shahriari, Noah Siegel, Caglar Gulcehre, Nicolas Heess, et al · 2020
Cited alongside, same era.
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 · 2020
Cited alongside, same era.
{OPAL}: Offline primitive discovery for accelerating offline reinforcement learning
Anurag Ajay, Aviral Kumar, Pulkit Agrawal, Sergey Levine, and Ofir Nachum · 2021
Cited alongside, same era.
A minimalist approach to offline reinforcement learning
Scott Fujimoto and Shixiang Shane Gu · 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.
Offline reinforcement learning with implicit q-learning
Ilya Kostrikov, Ashvin Nair, and Sergey Levine · 2022
Cited alongside, same era.
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Robocasa: Large-scale simulation of everyday tasks for generalist robots
Soroush Nasiriany, Abhiram Maddukuri, Lance Zhang, Adeet Parikh, Aaron Lo, Abhishek Joshi, Ajay Mandlekar, and Yuke Zhu · 2024
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Bigger, regularized, optimistic: scaling for compute and sample efficient continuous control
Michal Nauman, Mateusz Ostaszewski, Krzysztof Jankowski, Piotr Miłoś, and Marek Cygan · 2024
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Is value learning really the main bottleneck in offline rl?
Seohong Park, Kevin Frans, Sergey Levine, and Aviral Kumar · 2024
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Offline actor-critic reinforcement learning scales to large models
Jost Tobias Springenberg, Abbas Abdolmaleki, Jingwei Zhang, Oliver Groth, Michael Bloesch, Thomas Lampe, Philemon Brakel, Sarah Maria Elisabeth Bechtle, Steven Kapturowski, Roland Hafner, Nicolas Heess, and Martin Riedmiller · 2024
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Grape: Generalizing robot policy via preference alignment
Zijian Zhang, Kaiyuan Zheng, Zhaorun Chen, Joel Jang, Yi Li, Siwei Han, Chaoqi Wang, Mingyu Ding, Dieter Fox, and Huaxiu Yao · 2024
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Gr00t n1: An open foundation model for generalist humanoid robots
Johan Bjorck, Fernando Castañeda, Nikita Cherniadev, Xingye Da, Runyu Ding, Linxi Fan, Yu Fang, Dieter Fox, Fengyuan Hu, Spencer Huang, et al · 2025
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π 0 \pi_{0} : A vision-language-action flow model for general robot control
Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, et al · 2025
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Improving vision-language-action model with online reinforcement learning
Yanjiang Guo, Jianke Zhang, Xiaoyu Chen, Xiang Ji, Yen-Jen Wang, Yucheng Hu, and Jianyu Chen · 2025
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Dongchi Huang, Zhirui Fang, Tianle Zhang, Yihang Li, Lin Zhao, and Chunhe Xia · 2025
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π 0.5 \pi_{0.5} : a vision-language-action model with open-world generalization
Physical Intelligence, Kevin Black, Noah Brown, James Darpinian, Karan Dhabalia, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Manuel Y. Galliker, Dibya Ghosh, Lachy Groom, Karol Hausman, Brian Ichter, Szymon Jakubczak, Tim Jones, Liyiming Ke, Devin LeBlanc, Sergey Levine, Adrian Li-Bell, Mohith Mothukuri, Suraj Nair, Karl Pertsch, Allen Z. Ren, Lucy Xiaoyang Shi, Laura Smith, Jost Tobias Springenberg, Kyle Stachowicz, James Tanner, Quan Vuong, Homer Walke, Anna Walling, Haohuan Wang, Lili Yu, and Ury Zhilinsky · 2025
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Gr00t n1.5: An improved open foundation model for generalist humanoid robots
NVIDIA · 2025
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Coarse-to-fine q-network with action sequence for data-efficient robot learning
Younggyo Seo and Pieter Abbeel · 2025
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Ript-vla: Interactive post-training for vision-language-action models
Shuhan Tan, Kairan Dou, Yue Zhao, and Philipp Krähenbühl · 2025
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Chunking the critic: A transformer-based soft actor-critic with n-step returns
Dong Tian, Ge Li, Hongyi Zhou, Onur Celik, and Gerhard Neumann · 2025
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Reinbot: Amplifying robot visual-language manipulation with reinforcement learning
Hongyin Zhang, Zifeng Zhuang, Han Zhao, Pengxiang Ding, Hongchao Lu, and Donglin Wang · 2025
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