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There have been many recent advances on provably efficient Reinforcement Learning (RL) in problems with rich observation spaces.
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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Tracking in matrix systems
DJ Hartfiel · 1992
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Improving the cayley-hamilton equation for low-rank transformations
J Segercrantz · 1992
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Dense sets of diagonalizable matrices
Darald J Hartfiel · 1995
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Approximate planning in large pomdps via reusable trajectories
Michael J Kearns, Yishay Mansour, and Andrew Y Ng · 2000
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A natural policy gradient
Sham M Kakade · 2001
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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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Near-optimal reinforcement learning in polynomial time
Michael Kearns and Satinder Singh · 2002
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Concentration inequalities and model selection
Pascal Massart · 2007
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Near-optimal regret bounds for reinforcement learning
Thomas Jaksch, Ronald Ortner, and Peter Auer · 2010
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Pac bounds for discounted mdps
Tor Lattimore and Marcus Hutter · 2012
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Matrix analysis , volume 169
Rajendra Bhatia · 2013
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Guided policy search
Sergey Levine and Vladlen Koltun · 2013
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Model-based reinforcement learning and the eluder dimension
Ian Osband 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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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Cited alongside, same era.
Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
Cited alongside, same era.
Pac reinforcement learning with rich observations
Akshay Krishnamurthy, Alekh Agarwal, and John Langford · 2016
Cited alongside, same era.
Minimax regret bounds for reinforcement learning
Mohammad Gheshlaghi Azar, Ian Osband, and Rémi Munos · 2017
Cited alongside, same era.
Contextual decision processes with low Bellman rank are PAC-learnable
Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, and Robert E Schapire · 2017
Cited alongside, same era.
Bandit algorithms
Tor Lattimore and Csaba Szepesvári · 2020
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An improved analysis of (variance-reduced) policy gradient and natural policy gradient methods
Yanli Liu, Kaiqing Zhang, Tamer Basar, and Wotao Yin · 2020
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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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Sample efficient reinforcement learning via low-rank matrix estimation
Devavrat Shah, Dogyoon Song, Zhi Xu, and Yuzhe Yang · 2020
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John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Cited alongside, same era.
On oracle-efficient pac rl with rich observations
Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, and Robert E Schapire · 2018
Cited alongside, same era.
Breaking the curse of horizon: Infinite-horizon off-policy estimation
Qiang Liu, Lihong Li, Ziyang Tang, and Dengyong Zhou · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Politex: Regret bounds for policy iteration using expert prediction
Yasin Abbasi-Yadkori, Peter Bartlett, Kush Bhatia, Nevena Lazic, Csaba Szepesvari, and Gellért Weisz · 2019
Cited alongside, same era.
Global optimality guarantees for policy gradient methods
Jalaj Bhandari and Daniel Russo · 2019
Cited alongside, same era.
Policy certificates: Towards accountable reinforcement learning
Christoph Dann, Lihong Li, Wei Wei, and Emma Brunskill · 2019
Cited alongside, same era.
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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Andrea Zanette · 2020
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On the theory of policy gradient methods: Optimality, approximation, and distribution shift
Alekh Agarwal, Sham M Kakade, Jason D Lee, and Gaurav Mahajan · 2021
Closest in time.
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 S Du, Sham M Kakade, Jason D Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, and Ruosong Wang · 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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Jihao Long, Jiequn Han, and Weinan E · 2021
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Model-free representation learning and exploration in low-rank mdps
Aditya Modi, Jinglin Chen, Akshay Krishnamurthy, Nan Jiang, and Alekh Agarwal · 2021
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Exponential lower bounds for planning in mdps with linearly-realizable optimal action-value functions
Gellert Weisz, Philip Amortila, and Csaba Szepesvári · 2021
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