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We study the problem of representational transfer in RL, where an agent first pretrains in a number of source tasks to discover a shared representation, which is subsequently used to learn a good policy in a \emph{target task}.
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Contextual decision processes with low bellman rank are pac-learnable
Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, and Robert E Schapire · 2017
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Reinforcement learning: Theory and algorithms
Alekh Agarwal, Nan Jiang, Sham M Kakade, and Wen Sun · 2019
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Sharing knowledge in multi-task deep reinforcement learning
Carlo D’Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, and Jan Peters · 2019
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Provably efficient rl with rich observations via latent state decoding
Simon Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudik, and John Langford · 2019
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Continual lifelong learning with neural networks: A review
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Provable representation learning for imitation learning via bi-level optimization
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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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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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Lipschitz lifelong reinforcement learning
Erwan Lecarpentier, David Abel, Kavosh Asadi, Yuu Jinnai, Emmanuel Rachelson, and Michael L Littman · 2021
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Online model selection for reinforcement learning with function approximation
Jonathan Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, and Emma Brunskill · 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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Few-shot learning via learning the representation, provably
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Kinematic state abstraction and provably efficient rich-observation reinforcement learning
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Sample complexity of reinforcement learning using linearly combined model ensembles
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Regret bound balancing and elimination for model selection in bandits and rl
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On the theory of transfer learning: The importance of task diversity
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On reward-free reinforcement learning with linear function approximation
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Corruption-robust exploration in episodic reinforcement learning
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Model-free representation learning and exploration in low-rank mdps
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A free lunch from the noise: Provable and practical exploration for representation learning
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Learning one representation to optimize all rewards
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Representation learning for online and offline rl in low-rank mdps
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Cautiously optimistic policy optimization and exploration with linear function approximation
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Provably efficient representation learning in low-rank markov decision processes
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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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Reward-free rl is no harder than reward-aware rl in linear markov decision processes
Andrew Wagenmaker, Yifang Chen, Max Simchowitz, Simon S Du, and Kevin Jamieson · 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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Efficient reinforcement learning in block mdps: A model-free representation learning approach
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