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This work introduces Bilinear Classes, a new structural framework, which permit generalization in reinforcement learning in a wide variety of settings through the use of function approximation.
Efficient reinforcement learning in factored mdps
Michael Kearns and Daphne Koller · 1999
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Predictive representations of state
Michael L Littman, Richard S Sutton, and Satinder P Singh · 2001
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Predictive representations of state
Michael L Littman and Richard S Sutton · 2002
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Reinforcement learning with immediate rewards and linear hypotheses
Naoki Abe, Alan W Biermann, and Philip M Long · 2003
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Error bounds for approximate value iteration
Rémi Munos · 2005
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Flambe: Structural complexity and representation learning of low rank mdps
Alekh Agarwal, Sham Kakade, Akshay Krishnamurthy, and Wen Sun · 2006
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Stochastic linear optimization under bandit feedback
Varsha Dani, Thomas P Hayes, and Sham M Kakade · 2008
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A Unifying Framework for Computational Reinforcement Learning Theory
Lihong Li · 2009
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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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Improved algorithms for linear stochastic bandits
Yasin Abbasi-Yadkori, Dávid Pál, and Csaba Szepesvári · 2011
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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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Efficient exploration and value function generalization in deterministic systems
Zheng Wen and Benjamin Van Roy · 2013
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Learning to optimize via posterior sampling
Daniel Russo and Benjamin Van Roy · 2014
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Abstraction selection in model-based reinforcement learning
Nan Jiang, Alex Kulesza, and Satinder Singh · 2015
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Contextual decision processes with low bellman rank are pac-learnable, 2016
Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, and Robert E. Schapire · 2016
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Pac reinforcement learning with rich observations
Akshay Krishnamurthy, Alekh Agarwal, and John Langford · 2016
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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
Sample-optimal parametric Q-learning using linearly additive features
Lin Yang and Mengdi Wang · 2019
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Model-based reinforcement learning with value-targeted regression
Alex Ayoub, Zeyu Jia, Csaba Szepesvari, Mengdi Wang, 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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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
Sham Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi, and Wen Sun · 2020
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Mastering the game of go without human knowledge
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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Framework for control and deep reinforcement learning in traffic
Cathy Wu, Kanaad Parvate, Nishant Kheterpal, Leah Dickstein, Ankur Mehta, Eugene Vinitsky, and Alexandre M Bayen · 2017
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On the sample complexity of the linear quadratic regulator
Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, and Stephen Tu · 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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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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Kinematic state abstraction and provably efficient rich-observation reinforcement learning
Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, and John Langford · 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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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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Exponential lower bounds for planning in mdps with linearly-realizable optimal action-value functions, 2020
Gellert Weisz, Philip Amortila, and Csaba Szepesvári · 2020
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Zhuoran Yang, Chi Jin, Zhaoran Wang, Mengdi Wang, and Michael I Jordan · 2020
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Learning near optimal policies with low inherent bellman error, 2020
Andrea Zanette, Alessandro Lazaric, Mykel Kochenderfer, and Emma Brunskill · 2020
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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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