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There has been growing progress on theoretical analyses for provably efficient learning in MDPs with linear function approximation, but much of the existing work has made strong assumptions to enable exploration by conventional exploration frameworks.
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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The equivalence of two extremum problems
Jack Kiefer and Jacob Wolfowitz · 1960
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Markov Decision Processes: Discrete Stochastic Dynamic Programming
Martin L. Puterman · 1994
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Jean Tarbouriech, Matteo Pirotta, Michal Valko, and Alessandro Lazaric · 2002
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Michail G Lagoudakis and Ronald Parr · 2003
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Error bounds for approximate value iteration
Rémi Munos · 2005
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Provably efficient reinforcement learning with general value function approximation
Ruosong Wang, Ruslan Salakhutdinov, and Lin F. Yang · 2005
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On reward-free reinforcement learning with linear function approximation
Ruosong Wang, Simon S Du, Lin F Yang, and Ruslan Salakhutdinov · 2006
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Finite-time bounds for fitted value iteration
Rémi Munos and Csaba Szepesvári · 2008
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Near-optimal regret bounds for reinforcement learning
Thomas Jaksch, Ronald Ortner, and Peter Auer · 2010
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Improved algorithms for linear stochastic bandits
Yasin Abbasi-Yadkori, David Pal, and Csaba Szepesvari · 2011
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Finite-sample analysis of least-squares policy iteration
Alessandro Lazaric, Mohammad Ghavamzadeh, and Rémi Munos · 2012
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Thompson sampling for learning parameterized markov decision processes
Aditya Gopalan and Shie Mannor · 2015
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Pac reinforcement learning with rich observations
Akshay Krishnamurthy, Alekh Agarwal, and John Langford · 2016
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Shipra Agrawal and Randy Jia · 2017
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Daniel Russo · 2019
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High-dimensional statistics: A non-asymptotic viewpoint , volume 48
Martin J Wainwright · 2019
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Optimism in reinforcement learning with generalized linear function approximation
Yining Wang, Ruosong Wang, Simon S Du, and Akshay Krishnamurthy · 2019
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Tighter problem-dependent regret bounds in reinforcement learning without domain knowledge using value function bounds
Andrea Zanette and Emma Brunskill · 2019
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PC-PG: Policy cover directed exploration for provable policy gradient learning
Alekh Agarwal, Mikael Henaff, Sham Kakade, and Wen Sun · 2020
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Provably efficient maximum entropy exploration
Elad Hazan, Sham M. Kakade, Karan Singh, and Abby Van Soest · 2018
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Chi Jin, Zeyuan Allen-Zhu, Sebastien Bubeck, and Michael I Jordan · 2018
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Richard S Sutton and Andrew G Barto · 2018
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Christoph Dann, Lihong Li, Wei Wei, and Emma Brunskill · 2019
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Simon S. Du, Jason D. Lee, Gaurav Mahajan, and Ruosong Wang · 2020
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Adaptive reward-free exploration
Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson, Edouard Leurent, and Michal Valko · 2020
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