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We consider the hybrid reinforcement learning setting where the agent has access to both offline data and online interactive access.
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Combining self-supervised learning and imitation for vision-based rope manipulation
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Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
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Ashvin V Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, and Sergey Levine · 2018
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Learning montezuma’s revenge from a single demonstration
Tim Salimans and Richard Chen · 2018
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Generative adversarial imitation from observation
Faraz Torabi, Garrett Warnell, and Peter Stone · 2018
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Alekh Agarwal, Nan Jiang, Sham M Kakade, and Wen Sun · 2019
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Information-theoretic considerations in batch reinforcement learning
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Robonet: Large-scale multi-robot learning
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Avid: Learning multi-stage tasks via pixel-level translation of human videos
Laura Smith, Nikita Dhawan, Marvin Zhang, Pieter Abbeel, and Sergey Levine · 2019
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Provably efficient imitation learning from observation alone
Wen Sun, Anirudh Vemula, Byron Boots, and Drew Bagnell · 2019
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Is a good representation sufficient for sample efficient reinforcement learning?
Simon S Du, Sham M Kakade, Ruosong Wang, and Lin F Yang · 2020
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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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Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
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Kinematic state abstraction and provably efficient rich-observation reinforcement learning
Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, and John Langford · 2020
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Awac: Accelerating online reinforcement learning with offline datasets
Ashvin Nair, Abhishek Gupta, Murtaza Dalal, and Sergey Levine · 2020
A state-distribution matching approach to non-episodic reinforcement learning
Archit Sharma, Rehaan Ahmad, and Chelsea Finn · 2022
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Hybrid rl: Using both offline and online data can make rl efficient
Yuda Song, Yifei Zhou, Ayush Sekhari, J Andrew Bagnell, Akshay Krishnamurthy, and Wen Sun · 2022
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Efficient reinforcement learning in block mdps: A model-free representation learning approach
Xuezhou Zhang, Yuda Song, Masatoshi Uehara, Mengdi Wang, Alekh Agarwal, and Wen Sun · 2022
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Efficient online reinforcement learning with offline data
Philip J Ball, Laura Smith, Ilya Kostrikov, and Sergey Levine · 2023
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Provable guarantees for generative behavior cloning: Bridging low-level stability and high-level behavior
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Reinforcement learning with videos: Combining offline observations with interaction
Karl Schmeckpeper, Oleh Rybkin, Kostas Daniilidis, Sergey Levine, and Chelsea Finn · 2020
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Provably efficient model-based policy adaptation
Yuda Song, Aditi Mavalankar, Wen Sun, and Sicun Gao · 2020
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Off-policy imitation learning from observations
Zhuangdi Zhu, Kaixiang Lin, Bo Dai, and Jiayu Zhou · 2020
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Bilinear classes: A structural framework for provable generalization in RL
Simon S Du, Sham M Kakade, Jason D Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, and Ruosong Wang · 2021
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Bellman eluder dimension: New rich classes of RL problems, and sample-efficient algorithms
Chi Jin, Qinghua Liu, and Sobhan Miryoosefi · 2021
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Demodice: Offline imitation learning with supplementary imperfect demonstrations
Geon-Hyeong Kim, Seokin Seo, Jongmin Lee, Wonseok Jeon, HyeongJoo Hwang, Hongseok Yang, and Kee-Eung Kim · 2021
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State-only imitation learning for dexterous manipulation
Ilija Radosavovic, Xiaolong Wang, Lerrel Pinto, and Jitendra Malik · 2021
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Adam Block, Ali Jadbabaie, Daniel Pfrommer, Max Simchowitz, and Russ Tedrake · 2023
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Reinforcement learning from passive data via latent intentions
Dibya Ghosh, Chethan Anand Bhateja, and Sergey Levine · 2023
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Ego-exo4d: Understanding skilled human activity from first-and third-person perspectives
Kristen Grauman, Andrew Westbury, Lorenzo Torresani, Kris Kitani, Jitendra Malik, Triantafyllos Afouras, Kumar Ashutosh, Vijay Baiyya, Siddhant Bansal, Bikram Boote, et al · 2023
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Reboot: Reuse data for bootstrapping efficient real-world dexterous manipulation
Zheyuan Hu, Aaron Rovinsky, Jianlan Luo, Vikash Kumar, Abhishek Gupta, and Sergey Levine · 2023
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Alphastar unplugged: Large-scale offline reinforcement learning
Michaël Mathieu, Sherjil Ozair, Srivatsan Srinivasan, Caglar Gulcehre, Shangtong Zhang, Ray Jiang, Tom Le Paine, Richard Powell, Konrad Żołna, Julian Schrittwieser, et al · 2023
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Zakaria Mhammedi, Dylan J Foster, and Alexander Rakhlin · 2023
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Cal-ql: Calibrated offline rl pre-training for efficient online fine-tuning
Mitsuhiko Nakamoto, Yuexiang Zhai, Anikait Singh, Max Sobol Mark, Yi Ma, Chelsea Finn, Aviral Kumar, and Sergey Levine · 2023
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Open x-embodiment: Robotic learning datasets and rt-x models
Abhishek Padalkar, Acorn Pooley, Ajinkya Jain, Alex Bewley, Alex Herzog, Alex Irpan, Alexander Khazatsky, Anant Rai, Anikait Singh, Anthony Brohan, et al · 2023
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Fast imitation via behavior foundation models
Matteo Pirotta, Andrea Tirinzoni, Ahmed Touati, Alessandro Lazaric, and Yann Ollivier · 2023
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Hybrid inverse reinforcement learning
Juntao Ren, Gokul Swamy, S Zhiwei. Wu, J Andrew. Bagnell, and Choudhury Sanjiban · 2023
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Inverse reinforcement learning without reinforcement learning
Gokul Swamy, David Wu, Sanjiban Choudhury, Drew Bagnell, and Steven Wu · 2023
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Jump-start reinforcement learning
Ikechukwu Uchendu, Ted Xiao, Yao Lu, Banghua Zhu, Mengyuan Yan, Joséphine Simon, Matthew Bennice, Chuyuan Fu, Cong Ma, Jiantao Jiao, et al · 2023
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The virtues of laziness in model-based rl: A unified objective and algorithms
Anirudh Vemula, Yuda Song, Aarti Singh, Drew Bagnell, and Sanjiban Choudhury · 2023
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Leveraging offline data in online reinforcement learning
Andrew Wagenmaker and Aldo Pacchiano · 2023
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The role of coverage in online reinforcement learning
Tengyang Xie, Dylan J Foster, Yu Bai, Nan Jiang, and Sham M. Kakade · 2023
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Offline imitation learning with suboptimal demonstrations via relaxed distribution matching
Lantao Yu, Tianhe Yu, Jiaming Song, Willie Neiswanger, and Stefano Ermon · 2023
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Policy finetuning in reinforcement learning via design of experiments using offline data
Ruiqi Zhang and Andrea Zanette · 2023
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Offline data enhanced on-policy policy gradient with provable guarantees
Yifei Zhou, Ayush Sekhari, Yuda Song, and Wen Sun · 2023
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