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With the increasing need for handling large state and action spaces, general function approximation has become a key technique in reinforcement learning (RL).
Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm
Nick Littlestone · 1988
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Christopher John Cornish Hellaby Watkins · 1989
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Reinforcement learning applied to linear quadratic regulation
Steven Bradtke · 1992
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Integral probability metrics and their generating classes of functions
Alfred Müller · 1997
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Efficient noise-tolerant learning from statistical queries
Michael Kearns · 1998
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Policy gradient methods for reinforcement learning with function approximation
Richard S Sutton, David McAllester, Satinder Singh, and Yishay Mansour · 1999
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The nature of statistical learning theory
Vladimir Vapnik · 1999
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Rademacher and gaussian complexities: Risk bounds and structural results
Peter L Bartlett and Shahar Mendelson · 2002
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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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Optimal control: linear quadratic methods
Brian DO Anderson and John B Moore · 2007
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Near-optimal regret bounds for reinforcement learning
Peter Auer, Thomas Jaksch, and Ronald Ortner · 2008
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Stochastic linear optimization under bandit feedback
Varsha Dani, Thomas P Hayes, and Sham M Kakade · 2008
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Gaussian process optimization in the bandit setting: No regret and experimental design
Niranjan Srinivas, Andreas Krause, Sham M Kakade, and Matthias Seeger · 2009
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Online learning: Random averages, combinatorial parameters, and learnability
Alexander Rakhlin, Karthik Sridharan, and Ambuj Tewari · 2010
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Improved algorithms for linear stochastic bandits
Yasin Abbasi-Yadkori, Dávid Pál, and Csaba Szepesvári · 2011
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Convergence of stochastic processes
David Pollard · 2012
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Reinforcement learning in robotics: A survey
Jens Kober, J Andrew Bagnell, and Jan Peters · 2013
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Eluder dimension and the sample complexity of optimistic exploration
Daniel Russo and Benjamin Van Roy · 2013
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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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Model-based reinforcement learning and the eluder dimension
Ian Osband and Benjamin Van Roy · 2014
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Markov decision processes: discrete stochastic dynamic programming
Martin L Puterman · 2014
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Learning to optimize via posterior sampling
Daniel Russo and Benjamin Van Roy · 2014
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Sample complexity of episodic fixed-horizon reinforcement learning
Christoph Dann and Emma Brunskill · 2015
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Pac reinforcement learning with rich observations
Akshay Krishnamurthy, Alekh Agarwal, and John Langford · 2016
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Provably efficient exploration in policy optimization
Qi Cai, Zhuoran Yang, Chi Jin, and Zhaoran Wang · 2020
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On the sample complexity of the linear quadratic regulator
Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, and Stephen Tu · 2020
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Root-n-regret for learning in markov decision processes with function approximation and low bellman rank
Kefan Dong, Jian Peng, Yining Wang, and Yuan Zhou · 2020
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Model-based reinforcement learning with value-targeted regression
Zeyu Jia, Lin Yang, Csaba Szepesvari, and Mengdi Wang · 2020
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Provably efficient reinforcement learning with linear function approximation
Chi Jin, Zhuoran Yang, Zhaoran Wang, and Michael I Jordan · 2020
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Information theoretic regret bounds for online nonlinear control
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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Optimistic posterior sampling for reinforcement learning: worst-case regret bounds
Shipra Agrawal and Randy Jia · 2017
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Minimax regret bounds for reinforcement learning
Mohammad Gheshlaghi Azar, Ian Osband, and Rémi Munos · 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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Is Q-learning provably efficient?
Chi Jin, Zeyuan Allen-Zhu, Sebastien Bubeck, and Michael I Jordan · 2018
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Reinforcement Learning: An Introduction
Richard S Sutton and Andrew G Barto · 2018
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Sham Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi, and Wen Sun · 2020
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Active learning for nonlinear system identification with guarantees
Horia Mania, Michael I Jordan, and Benjamin Recht · 2020
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Sample complexity of reinforcement learning using linearly combined model ensembles
Aditya Modi, Nan Jiang, Ambuj Tewari, and Satinder Singh · 2020
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A unifying view of optimism in episodic reinforcement learning
Gergely Neu and Ciara Pike-Burke · 2020
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Reinforcement learning with general value function approximation: Provably efficient approach via bounded eluder dimension
Ruosong Wang, Russ R Salakhutdinov, and Lin Yang · 2020
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Reinforcement learning in feature space: Matrix bandit, kernels, and regret bound
Lin Yang and Mengdi Wang · 2020
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On function approximation in reinforcement learning: Optimism in the face of large state spaces
Zhuoran Yang, Chi Jin, Zhaoran Wang, Mengdi Wang, and Michael I Jordan · 2020
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A survey of autonomous driving: Common practices and emerging technologies
Ekim Yurtsever, Jacob Lambert, Alexander Carballo, and Kazuya Takeda · 2020
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Almost optimal model-free reinforcement learningvia reference-advantage decomposition
Zihan Zhang, Yuan Zhou, and Xiangyang Ji · 2020
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Episodic reinforcement learning in finite mdps: Minimax lower bounds revisited
Omar Darwiche Domingues, Pierre Ménard, Emilie Kaufmann, and Michal Valko · 2021
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Bilinear classes: A structural framework for provable generalization in rl
Simon Du, Sham Kakade, Jason Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, and Ruosong Wang · 2021
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The statistical complexity of interactive decision making
Dylan J Foster, Sham M Kakade, Jian Qian, and Alexander Rakhlin · 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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