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Combining offline and online reinforcement learning (RL) is crucial for efficient and safe learning.
Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Doubly robust off-policy value evaluation for reinforcement learning
Nan Jiang and Lihong Li · 2016
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world
Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel · 2017
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Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 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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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Implementation matters in deep rl: A case study on ppo and trpo
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, and Aleksander Madry · 2019
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Empirical study of off-policy policy evaluation for reinforcement learning
Cameron Voloshin, Hoang M Le, Nan Jiang, and Yisong Yue · 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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Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2020
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Awac: Accelerating online reinforcement learning with offline datasets
Ashvin Nair, Abhishek Gupta, Murtaza Dalal, and Sergey Levine · 2020
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Hyperparameter selection for offline reinforcement learning
Tom Le Paine, Cosmin Paduraru, Andrea Michi, Caglar Gulcehre, Konrad Zolna, Alexander Novikov, Ziyu Wang, and Nando de Freitas · 2020
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Offline rl without off-policy evaluation
David Brandfonbrener, Will Whitney, Rajesh Ranganath, and Joan Bruna · 2021
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Decision transformer: Reinforcement learning via sequence modeling
Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Misha Laskin, Pieter Abbeel, Aravind Srinivas, and Igor Mordatch · 2021
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Rvs: What is essential for offline rl via supervised learning?
Scott Emmons, Benjamin Eysenbach, Ilya Kostrikov, 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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Why generalization in rl is difficult: Epistemic pomdps and implicit partial observability
Dibya Ghosh, Jad Rahme, Aviral Kumar, Amy Zhang, Ryan P Adams, and Sergey Levine · 2021
Cited alongside, same era.
Offline reinforcement learning with implicit q-learning
Ilya Kostrikov, Ashvin Nair, and Sergey Levine · 2021
Cited alongside, same era.
Offline-to-online reinforcement learning via balanced replay and pessimistic q-ensemble
SeungHyun Lee, Younggyo Seo, Kimin Lee, Pieter Abbeel, and Jinwoo Shin · 2021
Cited alongside, same era.
Learning vision-guided quadrupedal locomotion end-to-end with cross-modal transformers
Ruihan Yang, Minghao Zhang, Nicklas Hansen, Huazhe Xu, and Xiaolong Wang · 2021
Cited alongside, same era.
Combo: Conservative offline model-based policy optimization
Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, and Chelsea Finn · 2021
A simple unified uncertainty-guided framework for offline-to-online reinforcement learning
Siyuan Guo, Yanchao Sun, Jifeng Hu, Sili Huang, Hechang Chen, Haiyin Piao, Lichao Sun, and Yi Chang · 2023
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Benchmarking offline reinforcement learning on real-robot hardware
Nico Gürtler, Sebastian Blaes, Pavel Kolev, Felix Widmaier, Manuel Wüthrich, Stefan Bauer, Bernhard Schölkopf, and Georg Martius · 2023
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Beyond uniform sampling: Offline reinforcement learning with imbalanced datasets
Zhang-Wei Hong, Aviral Kumar, Sathwik Karnik, Abhishek Bhandwaldar, Akash Srivastava, Joni Pajarinen, Romain Laroche, Abhishek Gupta, and Pulkit Agrawal · 2023
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Decision transformer under random frame dropping
Kaizhe Hu, Ray Chen Zheng, Yang Gao, and Huazhe Xu · 2023
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Cited alongside, same era.
Adversarially trained actor critic for offline reinforcement learning
Ching-An Cheng, Tengyang Xie, Nan Jiang, and Alekh Agarwal · 2022
Cited alongside, same era.
Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
Cited alongside, same era.
Confidence-conditioned value functions for offline reinforcement learning
Joey Hong, Aviral Kumar, and Sergey Levine · 2022
Cited alongside, same era.
When to trust your simulator: Dynamics-aware hybrid offline-and-online reinforcement learning
Haoyi Niu, Yiwen Qiu, Ming Li, Guyue Zhou, Jianming HU, Xianyuan Zhan, et al · 2022
Cited alongside, same era.
Learning to walk in minutes using massively parallel deep reinforcement learning
Nikita Rudin, David Hoeller, Philipp Reist, and Marco Hutter · 2022
Cited alongside, same era.
Legged robots that keep on learning: Fine-tuning locomotion policies in the real world
Laura Smith, J Chase Kew, Xue Bin Peng, Sehoon Ha, Jie Tan, and Sergey Levine · 2022
Cited alongside, same era.
CORL: Research-oriented deep offline reinforcement learning library
Denis Tarasov, Alexander Nikulin, Dmitry Akimov, Vladislav Kurenkov, and Sergey Kolesnikov · 2022
Cited alongside, same era.
Jianxiong Li, Xiao Hu, Haoran Xu, Jingjing Liu, Xianyuan Zhan, and Ya-Qin Zhang · 2023
Closest in time.
SpawnNet: Learning Generalizable Visuomotor Skills from Pre-trained Networks
Xingyu Lin, John So, Sashwat Mahalingam, Fangchen Liu, and Pieter Abbeel · 2023
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Walk these ways: Tuning robot control for generalization with multiplicity of behavior
Gabriel B Margolis and Pulkit Agrawal · 2023
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Learning on the job: self-rewarding offline-to-online finetuning for industrial insertion of novel connectors from vision
Ashvin Nair, Brian Zhu, Gokul Narayanan, Eugen Solowjow, and Sergey Levine · 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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Grow your limits: Continuous improvement with real-world rl for robotic locomotion
Laura Smith, Yunhao Cao, and Sergey Levine · 2023
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Manipulate by seeing: Creating manipulation controllers from pre-trained representations
Jianren Wang, Sudeep Dasari, Mohan Kumar Srirama, Shubham Tulsiani, and Abhinav Gupta · 2023
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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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Actor-critic alignment for offline-to-online reinforcement learning
Zishun Yu and Xinhua Zhang · 2023
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Policy expansion for bridging offline-to-online reinforcement learning
Haichao Zhang, We Xu, and Haonan Yu · 2023
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Adaptive policy learning for offline-to-online reinforcement learning
Han Zheng, Xufang Luo, Pengfei Wei, Xuan Song, Dongsheng Li, and Jing Jiang · 2023
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Real world offline reinforcement learning with realistic data source
Gaoyue Zhou, Liyiming Ke, Siddhartha Srinivasa, Abhinav Gupta, Aravind Rajeswaran, and Vikash Kumar · 2023
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Behavior proximal policy optimization
Zifeng Zhuang, Kun Lei, Jinxin Liu, Donglin Wang, and Yilang Guo · 2023
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