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This work studies the question of Representation Learning in RL: how can we learn a compact low-dimensional representation such that on top of the representation we can perform RL procedures such as exploration and exploitation, in a sample efficient manner.
Is a good representation sufficient for sample efficient reinforcement learning?
Simon S Du, Sham M Kakade, Ruosong Wang, and Lin F Yang · 1910
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R-max-a general polynomial time algorithm for near-optimal reinforcement learning
Ronen I Brafman and Moshe Tennenholtz · 2002
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Approximately optimal approximate reinforcement learning
Sham Kakade and John Langford · 2002
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Learning near-optimal policies with bellman-residual minimization based fitted policy iteration and a single sample path
András Antos, Csaba Szepesvári, and Rémi Munos · 2008
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Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky · 2009
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Regret bounds for the adaptive control of linear quadratic systems
Yasin Abbasi-Yadkori and Csaba Szepesvári · 2011
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Is pessimism provably efficient for offline rl?
Ying Jin, Zhuoran Yang, and Zhaoran Wang · 2012
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Taming the monster: A fast and simple algorithm for contextual bandits
Alekh Agarwal, Daniel Hsu, Satyen Kale, John Langford, Lihong Li, and Robert Schapire · 2014
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Learning to optimize via posterior sampling
Daniel Russo and Benjamin Van Roy · 2014
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Oracle-efficient online learning and auction design
Miroslav Dudík, Nika Haghtalab, Haipeng Luo, Robert E Schapire, Vasilis Syrgkanis, and Jennifer Wortman Vaughan · 2017
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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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On oracle-efficient pac rl with rich observations
Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, and Robert E Schapire · 2018
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A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al · 2018
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Information-theoretic considerations in batch reinforcement learning
Jinglin Chen and Nan Jiang · 2019
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Online learning in kernelized markov decision processes
Sayak Ray Chowdhury and Aditya Gopalan · 2019
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Model-based rl in contextual decision processes: Pac bounds and exponential improvements over model-free approaches
Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, and John Langford · 2019
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Efficient model-based reinforcement learning through optimistic policy search and planning
Sebastian Curi, Felix Berkenkamp, and Andreas Krause · 2020
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Provably efficient exploration for reinforcement learning using unsupervised learning
Fei Feng, Ruosong Wang, Wotao Yin, Simon S Du, and Lin F Yang · 2020
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Beyond ucb: Optimal and efficient contextual bandits with regression oracles
Dylan Foster and Alexander Rakhlin · 2020
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Dylan J Foster, Alexander Rakhlin, David Simchi-Levi, and Yunzong Xu · 2020
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Information theoretic regret bounds for online nonlinear control
Sham Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi, and Wen Sun · 2020
On the theory of policy gradient methods: Optimality, approximation, and distribution shift
Alekh Agarwal, Sham M Kakade, Jason D Lee, and Gaurav Mahajan · 2021
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Mitigating covariate shift in imitation learning via offline data without great coverage
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Agnostic reinforcement learning with low-rank mdps and rich observations
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Bilinear classes: A structural framework for provable generalization in rl
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Corruption-robust exploration in episodic reinforcement learning
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Active learning for nonlinear system identification with guarantees
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Pessimistic model-based offline rl: Pac bounds and posterior sampling under partial coverage
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Exponential lower bounds for planning in mdps with linearly-realizable optimal action-value functions
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Bellman-consistent pessimism for offline reinforcement learning
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