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While multitask representation learning has become a popular approach in reinforcement learning (RL) to boost the sample efficiency, the theoretical understanding of why and how it works is still limited.
Multitask learning
Rich Caruana · 1997
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Jonathan Baxter · 2000
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Bounds for linear multi-task learning
Andreas Maurer · 2006
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Transfer learning for reinforcement learning domains: A survey
Matthew E Taylor and Peter Stone · 2009
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Linear algorithms for online multitask classification
Giovanni Cavallanti, Nicolo Cesa-Bianchi, and Claudio Gentile · 2010
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The mnist database of handwritten digit images for machine learning research [best of the web]
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Fast and accurate matrix completion via truncated nuclear norm regularization
Yao Hu, Debing Zhang, Jieping Ye, Xuelong Li, and Xiaofei He · 2013
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Eluder dimension and the sample complexity of optimistic exploration
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Alekh Agarwal, Daniel Hsu, Satyen Kale, John Langford, Lihong Li, and Robert Schapire · 2014
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Actor-mimic: Deep multitask and transfer reinforcement learning
Emilio Parisotto, Jimmy Lei Ba, and Ruslan Salakhutdinov · 2015
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Andrei A Rusu, Sergio Gomez Colmenarejo, Caglar Gulcehre, Guillaume Desjardins, James Kirkpatrick, Razvan Pascanu, Volodymyr Mnih, Koray Kavukcuoglu, and Raia Hadsell · 2015
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Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
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Lydia T Liu, Urun Dogan, and Katja Hofmann · 2016
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The benefit of multitask representation learning
Andreas Maurer, Massimiliano Pontil, and Bernardino Romera-Paredes · 2016
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Hypothesis transfer learning via transformation functions
Simon S Du, Jayanth Koushik, Aarti Singh, and Barnabás Póczos · 2017
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Contextual decision processes with low bellman rank are PAC-learnable
Provable representation learning for imitation learning via bi-level optimization
Sanjeev Arora, Simon S Du, Sham Kakade, Yuping Luo, and Nikunj Saunshi · 2020
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Sharing knowledge in multi-task deep reinforcement learning
Carlo D’Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, and Jan Peters · 2020
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Few-shot learning via learning the representation, provably
Simon S Du, Wei Hu, Sham M Kakade, Jason D Lee, and Qi Lei · 2020
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Provable meta-learning of linear representations
Nilesh Tripuraneni, Chi Jin, and Michael I Jordan · 2020
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Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, and Robert E Schapire · 2017
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David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
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Distral: Robust multitask reinforcement learning
Yee Teh, Victor Bapst, Wojciech M Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, and Razvan Pascanu · 2017
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Model-based reinforcement learning in contextual decision processes
Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, and John Langford · 2018
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Gradient descent finds global minima of deep neural networks
Simon Du, Jason Lee, Haochuan Li, Liwei Wang, and Xiyu Zhai · 2019
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Ruosong Wang, Ruslan Salakhutdinov, and Lin F Yang · 2020
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Learning near optimal policies with low inherent bellman error
Andrea Zanette, Alessandro Lazaric, Mykel Kochenderfer, and Emma Brunskill · 2020
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Kefan Dong, Jiaqi Yang, and Tengyu Ma · 2021
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Near-optimal representation learning for linear bandits and linear rl
Jiachen Hu, Xiaoyu Chen, Chi Jin, Lihong Li, and Liwei 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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On the power of multitask representation learning in linear mdp
Rui Lu, Gao Huang, and Simon S Du · 2021
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Reinforcement learning in linear mdps: Constant regret and representation selection
Matteo Papini, Andrea Tirinzoni, Aldo Pacchiano, Marcello Restelli, Alessandro Lazaric, and Matteo Pirotta · 2021
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Impact of representation learning in linear bandits
Jiaqi Yang, Wei Hu, Jason D. Lee, and Simon Shaolei Du · 2021
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Provable benefit of multitask representation learning in reinforcement learning
Yuan Cheng, Songtao Feng, Jing Yang, Hong Zhang, and Yingbin Liang · 2022
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Nearly minimax algorithms for linear bandits with shared representation
Jiaqi Yang, Qi Lei, Jason D Lee, and Simon S Du · 2022
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