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Meticulously analysing the empirical strengths and weaknesses of reinforcement learning methods in hard (challenging) environments is essential to inspire innovations and assess progress in the field.
A Markovian decision process
Richard Bellman · 1957
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Hierarchical reinforcement learning with the MAXQ value function decomposition
Thomas G Dietterich · 2000
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Classification problems in MDPs
LCM Kallenberg · 2002
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Near-optimal reinforcement learning in polynomial time
Michael Kearns and Satinder Singh · 2002
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On the sample complexity of reinforcement learning
Sham Machandranath Kakade · 2003
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Logarithmic online regret bounds for undiscounted reinforcement learning
Peter Auer and Ronald Ortner · 2006
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An analysis of model-based interval estimation for Markov decision processes
Alexander L Strehl and Michael L Littman · 2008
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Probabilistic graphical models: principles and techniques
D. Koller and N. Friedman · 2009
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REGAL: a regularization based algorithm for reinforcement learning in weakly communicating MDPs
Peter Bartlett and Ambuj Tewari · 2009
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Near-optimal regret bounds for reinforcement learning
Thomas Jaksch, Ronald Ortner, and Peter Auer · 2010
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Action-gap phenomenon in reinforcement learning
Amir Massoud Farahmand · 2011
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(More) Efficient reinforcement learning via posterior sampling
Ian Osband, Daniel Russo, and Benjamin Van Roy · 2013
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Yin Tat Lee and Aaron Sidford · 2013
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Markov decision processes: discrete stochastic dynamic programming
Martin L Puterman · 2014
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How hard is my MDP? The distribution-norm to the rescue"
Odalric-Ambrym Maillard, Timothy A Mann, and Shie Mannor · 2014
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Sample complexity of episodic fixed-horizon reinforcement learning
Christoph Dann and Emma Brunskill · 2015
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Markov chains and mixing times
David A Levin and Yuval Peres · 2017
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Posterior sampling for reinforcement learning: worst-case regret bounds
Shipra Agrawal and Randy Jia · 2017
MDP playground: A design and debug testbed for reinforcement learning
Raghu Rajan, Jessica Lizeth Borja Diaz, Suresh Guttikonda, Fabio Ferreira, André Biedenkapp, Jan Ole von Hartz, and Frank Hutter · 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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Non-asymptotic gap-dependent regret bounds for tabular MDPs
Max Simchowitz and Kevin G Jamieson · 2019
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Deep exploration via randomized value functions
Ian Osband, Benjamin Van Roy, Daniel J Russo, Zheng Wen, et al · 2019
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Provably efficient RL with rich observations via latent state decoding
Simon Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudik, and John Langford · 2019
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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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Minimalistic Gridworld environment for OpenAI gym
Maxime Chevalier-Boisvert, Lucas Willems, and Suman Pal · 2018
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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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Aaron Sidford, Mengdi Wang, Xian Wu, Lin Yang, and Yinyu Ye · 2018
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Grandmaster level in StarCraft II using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al · 2019
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Dota 2 with large scale deep reinforcement learning
Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemysław Dębiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Chris Hesse, et al · 2019
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Estimating the mixing time of ergodic Markov chains
Geoffrey Wolfer and Aryeh Kontorovich · 2019
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Behaviour suite for reinforcement learning
Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvári, Satinder Singh, Benjamin Van Roy, Richard Sutton, David Silver, and Hado van Hasselt · 2020
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Model-free reinforcement learning in infinite-horizon average-reward Markov decision processes
Chen-Yu Wei, Mehdi Jafarnia Jahromi, Haipeng Luo, Hiteshi Sharma, and Rahul Jain · 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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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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Beyond no regret: Instance-dependent pac reinforcement learning
Andrew J Wagenmaker, Max Simchowitz, and Kevin Jamieson · 2022
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