Fetching the paper…
Reading the bibliography…
Many reinforcement learning (RL) problems in practice are offline, learning purely from observational data.
Robust Estimation of a Location Parameter
Peter J. Huber · 1964
Earlier work this paper cites.
Advances in prospect theory: Cumulative representation of uncertainty
Amos Tversky and Daniel Kahneman · 1992
Earlier work this paper cites.
Issues in using function approximation for reinforcement learning
Sebastian Thrun and Anton Schwartz · 1993
Earlier work this paper cites.
Eligibility traces for off-policy policy evaluation
Doina Precup · 2000
Earlier work this paper cites.
A class of distortion operators for pricing financial and insurance risks
Shaun S Wang · 2000
Earlier work this paper cites.
Quantile regression
Roger Koenker and Kevin F Hallock · 2001
Earlier work this paper cites.
Conditional value-at-risk for general loss distributions
R Tyrrell Rockafellar and Stanislav Uryasev · 2002
Earlier work this paper cites.
Tree-based batch mode reinforcement learning
Damien Ernst, Pierre Geurts, and Louis Wehenkel · 2005
Earlier work this paper cites.
Neural fitted q iteration–first experiences with a data efficient neural reinforcement learning method
Martin Riedmiller · 2005
Earlier work this paper cites.
Maximum entropy inverse reinforcement learning
Brian D Ziebart, Andrew L Maas, J Andrew Bagnell, and Anind K Dey · 2008
Earlier work this paper cites.
Thomas Degris, Martha White, and Richard S Sutton · 2012
Earlier work this paper cites.
Batch reinforcement learning
Sascha Lange, Thomas Gabel, and Martin Riedmiller · 2012
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
Earlier work this paper cites.
Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
Earlier work this paper cites.
Markov decision processes: discrete stochastic dynamic programming
Martin L Puterman · 2014
Earlier work this paper cites.
Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
Cited alongside, same era.
Doubly robust off-policy value evaluation for reinforcement learning, 2016
Nan Jiang and Lihong Li · 2016
Cited alongside, same era.
Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
Cited alongside, same era.
Data-efficient off-policy policy evaluation for reinforcement learning
Philip Thomas and Emma Brunskill · 2016
Cited alongside, same era.
Deep reinforcement learning with double q-learning
Hado Van Hasselt, Arthur Guez, and David Silver · 2016
Cited alongside, same era.
A distributional perspective on reinforcement learning
A comparative analysis of expected and distributional reinforcement learning
Clare Lyle, Marc G Bellemare, and Pablo Samuel Castro · 2019
Later among the works it cites.
Statistics and samples in distributional reinforcement learning
Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G Bellemare, and Will Dabney · 2019
Later among the works it cites.
Yichuan Charlie Tang, Jian Zhang, and Ruslan Salakhutdinov · 2019
Later among the works it cites.
Behavior regularized offline reinforcement learning
Yifan Wu, George Tucker, and Ofir Nachum · 2019
Later among the works it cites.
Fully parameterized quantile function for distributional reinforcement learning
Derek Yang, Li Zhao, Zichuan Lin, Tao Qin, Jiang Bian, and Tieyan Liu · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Marc G Bellemare, Will Dabney, and Rémi Munos · 2017
Cited alongside, same era.
Risk-constrained reinforcement learning with percentile risk criteria
Yinlam Chow, Mohammad Ghavamzadeh, Lucas Janson, and Marco Pavone · 2017
Cited alongside, same era.
Distributed distributional deterministic policy gradients
Gabriel Barth-Maron, Matthew W Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva Tb, Alistair Muldal, Nicolas Heess, and Timothy Lillicrap · 2018
Cited alongside, same era.
Implicit quantile networks for distributional reinforcement learning
Will Dabney, Georg Ostrovski, David Silver, and Rémi Munos · 2018
Cited alongside, same era.
Distributional reinforcement learning with quantile regression
Will Dabney, Mark Rowland, Marc Bellemare, and Rémi Munos · 2018
Cited alongside, same era.
Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke Hoof, and David Meger · 2018
Cited alongside, same era.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
Cited alongside, same era.
Later among the works it cites.
An optimistic perspective on offline reinforcement learning
Rishabh Agarwal, Dale Schuurmans, and Mohammad Norouzi · 2020
Later among the works it cites.
D4rl: Datasets for deep data-driven reinforcement learning
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
Later among the works it cites.
Being optimistic to be conservative: Quickly learning a cvar policy
Ramtin Keramati, Christoph Dann, Alex Tamkin, and Emma Brunskill · 2020
Later among the works it cites.
Morel: Model-based offline reinforcement learning
Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, and Thorsten Joachims · 2020
Later among the works it cites.
Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2020
Later among the works it cites.
Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
Later among the works it cites.
Distributional soft actor critic for risk sensitive learning
Xiaoteng Ma, Qiyuan Zhang, Li Xia, Zhengyuan Zhou, Jun Yang, and Qianchuan Zhao · 2020
Later among the works it cites.
Distributional reinforcement learning via moment matching, 2020
Thanh Tang Nguyen, Sunil Gupta, and Svetha Venkatesh · 2020
Later among the works it cites.
Mopo: Model-based offline policy optimization
Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Y Zou, Sergey Levine, Chelsea Finn, and Tengyu Ma · 2020
Later among the works it cites.
State relevance for off-policy evaluation, 2021
Simon P. Shen, Yecheng Jason Ma, Omer Gottesman, and Finale Doshi-Velez · 2021
Closest in time.
Risk-averse offline reinforcement learning
Núria Armengol Urpí, Sebastian Curi, and Andreas Krause · 2021
Closest in time.