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The modern paradigm in machine learning involves pre-training on diverse data, followed by task-specific fine-tuning.
Continual learning in reinforcement environments
Mark Bishop Ring · 1994
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Stefan Schaal · 1996
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D4RL: Datasets for deep data-driven reinforcement learning
J. Fu, A. Kumar, O. Nachum, G. Tucker, and S. Levine · 2004
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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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Policy search for motor primitives in robotics
Jens Kober and Jan Peters · 2008
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Double q-learning
Hado van Hasselt · 2010
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Rl 2 : Fast reinforcement learning via slow reinforcement learning
Yan Duan, John Schulman, Xi Chen, Peter L Bartlett, Ilya Sutskever, and Pieter Abbeel · 2016
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
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Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, and Sergey Levine · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Promp: Proximal meta-policy search
Jonas Rothfuss, Dennis Lee, Ignasi Clavera, Tamim Asfour, and Pieter Abbeel · 2018
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Some considerations on learning to explore via meta-reinforcement learning
Bradly C Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, and Ilya Sutskever · 2018
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Diagnosing bottlenecks in deep q-learning algorithms
Justin Fu, Aviral Kumar, Matthew Soh, and Sergey Levine · 2019
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Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning
Abhishek Gupta, Vikash Kumar, Corey Lynch, Sergey Levine, and Karol Hausman · 2019
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Efficient off-policy meta-reinforcement learning via probabilistic context variables
Kate Rakelly, Aurick Zhou, Chelsea Finn, Sergey Levine, and Deirdre Quillen · 2019
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Meta reinforcement learning for sim-to-real domain adaptation
Karol Arndt, Murtaza Hazara, Ali Ghadirzadeh, and Ville Kyrki · 2020
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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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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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Randomized ensembled double q-learning: Learning fast without a model
Xinyue Chen, Che Wang, Zijian Zhou, and Keith W. Ross · 2021
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Offline meta reinforcement learning–identifiability challenges and effective data collection strategies
Ron Dorfman, Idan Shenfeld, and Aviv Tamar · 2021
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A minimalist approach to offline reinforcement learning
Scott Fujimoto and Shixiang Shane Gu · 2021
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Continual model-based reinforcement learning with hypernetworks
Yizhou Huang, Kevin Xie, Homanga Bharadhwaj, and Florian Shkurti · 2021
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Amago: Scalable in-context reinforcement learning for adaptive agents
Jake Grigsby, Linxi Fan, and Yuke Zhu · 2023
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Imitation bootstrapped reinforcement learning
Hengyuan Hu, Suvir Mirchandani, and Dorsa Sadigh · 2023
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Seizing serendipity: Exploiting the value of past success in off-policy actor-critic
Tianying Ji, Yu Luo, Fuchun Sun, Xianyuan Zhan, Jianwei Zhang, and Huazhe Xu · 2023
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Finetuning from offline reinforcement learning: Challenges, trade-offs and practical solutions
Yicheng Luo, Jackie Kay, Edward Grefenstette, and Marc Peter Deisenroth · 2023
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Moto: Offline pre-training to online fine-tuning for model-based robot learning
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Offline reinforcement learning with fisher divergence critic regularization
Ilya Kostrikov, Jonathan Tompson, Rob Fergus, and Ofir Nachum · 2021
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The difficulty of passive learning in deep reinforcement learning
Georg Ostrovski, Pablo Samuel Castro, and Will Dabney · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Continual world: A robotic benchmark for continual reinforcement learning
Maciej Wołczyk, Michał Zając, Razvan Pascanu, Łukasz Kuciński, and Piotr Miłoś · 2021
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Policy finetuning: Bridging sample-efficient offline and online reinforcement learning
Tengyang Xie, Nan Jiang, Huan Wang, Caiming Xiong, and Yu Bai · 2021
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Reincarnating reinforcement learning: Reusing prior computation to accelerate progress
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C Courville, and Marc Bellemare · 2022
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Adversarially Trained Actor Critic for Offline RL
C. Cheng, T. Xie, N. Jiang, and A. Agarwal · 2022
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Rafael Rafailov, Kyle Beltran Hatch, Victor Kolev, John D Martin, Mariano Phielipp, and Chelsea Finn · 2023
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Hybrid RL: Using both offline and online data can make RL efficient
Yuda Song, Yifei Zhou, Ayush Sekhari, Drew Bagnell, Akshay Krishnamurthy, and Wen Sun · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 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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Sigmoid loss for language image pre-training
Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer · 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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Offline data enhanced on-policy policy gradient with provable guarantees
Yifei Zhou, Ayush Sekhari, Yuda Song, and Wen Sun · 2023
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Cal-ql: Calibrated offline rl pre-training for efficient online fine-tuning
Mitsuhiko Nakamoto, Simon Zhai, Anikait Singh, Max Sobol Mark, Yi Ma, Chelsea Finn, Aviral Kumar, and Sergey Levine · 2024
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Diffusion policy policy optimization
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Revisiting the minimalist approach to offline reinforcement learning
Denis Tarasov, Vladislav Kurenkov, Alexander Nikulin, and Sergey Kolesnikov · 2024
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Fine-tuning reinforcement learning models is secretly a forgetting mitigation problem
Maciej Wołczyk, Bartłomiej Cupiał, Mateusz Ostaszewski, Michał Bortkiewicz, Michał Zając, Razvan Pascanu, Łukasz Kuciński, and Piotr Miłoś · 2024
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A perspective of q-value estimation on offline-to-online reinforcement learning
Yinmin Zhang, Jie Liu, Chuming Li, Yazhe Niu, Yaodong Yang, Yu Liu, and Wanli Ouyang · 2024
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Autonomous improvement of instruction following skills via foundation models
Zhiyuan Zhou, Pranav Atreya, Abraham Lee, Homer Walke, Oier Mees, and Sergey Levine · 2024
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Fmb: a functional manipulation benchmark for generalizable robotic learning
Jianlan Luo, Charles Xu, Fangchen Liu, Liam Tan, Zipeng Lin, Jeffrey Wu, Pieter Abbeel, and Sergey Levine · 2025
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