@misc{levine2020offline,
      title={Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems}, 
      author={Sergey Levine and Aviral Kumar and George Tucker and Justin Fu},
      year={2020},
      eprint={2005.01643},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@InProceedings{haarnoja18bSA,
title = {Soft {A}ctor-{C}ritic: {O}ff-{P}olicy {M}aximum {E}ntropy {D}eep {R}einforcement {L}earning with a {S}tochastic {A}ctor},
author = {Haarnoja, T. and Zhou, A. and Abbeel, P. and Levine, S.},
booktitle = {Proceedings of the 35th International Conference on Machine Learning},
pages = {1861--1870},
year = {2018}
}

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    author = {Vijay Konda and John Tsitsiklis},
    title = {Actor-Critic Algorithms},
    booktitle = {SIAM Journal on Control and Optimization},
    year = {2000},
    pages = {1008--1014},
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}

@article{Steimle2018MM,
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  author={Lauren Steimle and David Kaufman and Brian Denton},
  journal = {Optimization Online},
  year={2018}
}

@article{bucholz2019CMDP,
author = {Buchholz, Peter and Scheftelowitsch, Dimitri},
year = {2019},
title = {Computation of weighted sums of rewards for concurrent {MDPs}},
volume = {89},
journal = {Mathematical Methods of Operations Research},
}

@article{Hoffman2013SVI,
author = {Hoffman, Matthew D. and Blei, David M. and Wang, Chong and Paisley, John},
title = {Stochastic Variational Inference},
year = {2013},
publisher = {JMLR.org},
volume = {14},
number = {1},
journal = {Journal of Machine Learning Research},
pages = {1303–1347},
}
@article{Plummer2003JAGS,
author = {Plummer, Martyn},
year = {2003},
title = {JAGS: A Program for Analysis of Bayesian Graphical Models using Gibbs Sampling},
volume = {124},
journal = {3rd International Workshop on Distributed Statistical Computing (DSC 2003); Vienna, Austria}
}
@article{hoffman2011nouturn,
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      year={2011},
      journal={Preprint arXiv:1111.4246},
}
@article{hester2017DQ,
      title={Deep {Q}-learning from Demonstrations},
      author={Todd Hester and Matej Vecerik and Olivier Pietquin and Marc Lanctot and Tom Schaul and Bilal Piot and Dan Horgan and John Quan and Andrew Sendonaris and Gabriel Dulac-Arnold and Ian Osband and John Agapiou and Joel Z. Leibo and Audrunas Gruslys},
      year={2017},
      journal={Preprint arXiv:1704.03732}
}
@article{lillicrap2019DDPG,
      title={Continuous control with deep reinforcement learning},
      author={Timothy P. Lillicrap and Jonathan J. Hunt and Alexander Pritzel and Nicolas Heess and Tom Erez and Yuval Tassa and David Silver and Daan Wierstra},
      year={2019},
      journal={Preprint arXiv:1509.02971},
}
@article{Box1973BayesianLR,
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@inbook{bucholz2020CMFMP,
author = {Buchholz, Peter and Scheftelowitsch, Dimitri},
year = {2020},
pages = {37-53},
title = {Concurrent MDPs with Finite Markovian Policies},
publisher = {Springer, Cham},
}

@article{merakli2019RiskA,
author = {Meraklı, Merve and Küçükyavuz, Simge},
year = {2019},
pages = {1-52},
title = {Risk Aversion to Parameter Uncertainty in {Markov} Decision Processes with an Application to Slow-Onset Disaster Relief},
journal = {IISE Transactions},
}
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year = {2003},
title = {Portfolio Optimization With Conditional Value-at-Risk Objective and Constraints},
volume = {4},
journal = {Journal of Risk},
}

@inproceedings{Mannor2012LightningDN,
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    booktitle = {International {{Conference}} on {{Machine Learning}} ({{ICML}})},
    author = {Mannor, Shie and Mebel, O and Xu, H},
    year = {2012},
    file = {/home/marek/Documents/Zotero/storage/RZHH7UJY/Mannor, Mebel, Xu - 2012 - Lightning does not strike twice Robust MDPs with coupled uncertainty.pdf}
}


@article{tang2019worst,
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      author={Yichuan Charlie Tang and Jian Zhang and Ruslan Salakhutdinov},
      year={2019},
      journal={Preprint arXiv:1911.03618},
}
@article{epstein2003RecursiveMP,
title = {Recursive multiple-priors},
journal = {Journal of Economic Theory},
volume = {113},
number = {1},
pages = {1-31},
year = {2003},
}

@inproceedings{russel2019BeyondCR,
  author    = {Reazul Hasan Russel and Marek Petrik},
  title     = {Beyond Confidence Regions: Tight {Bayesian} Ambiguity Sets for Robust
               {MDPs}},
  booktitle = {International Conference on Neural Information Processing Systems},
  year = {2019}
}

@inproceedings{Derman2019BayesianRobust,
  author    = {Esther Derman and
               Daniel J. Mankowitz and
               Timothy A. Mann and
               Shie Mannor},
  title     = {A {Bayesian} Approach to Robust Reinforcement Learning},
  booktitle = {Conference on Uncertainty in Artificial Intelligence},
  year      = {2019},
  eprint    = {1905.08188}
}

@INPROCEEDINGS{Wang2007DualR,
  author={T. {Wang} and M. {Bowling} and D. {Schuurmans}},
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  year={2007},
  pages={44-51},
}
@article{gupta2019NearOpt,
author = {Gupta, Vishal},
year = {2019},
title = {Near-Optimal {Bayesian} Ambiguity Sets for Distributionally Robust Optimization},
journal = {Management Science},
}
@inproceedings{Petrik2016SafePI,
 author = {Petrik, Marek and Ghavamzadeh, Mohammad and Chow, Yinlam},
 title = {Safe Policy Improvement by Minimizing Robust Baseline Regret},
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 year = {2016},
 pages = {2306--2314},
}

@book{bersekas2007DP,
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year = {2007},
publisher = {Athena Scientific},
edition = {3rd},
}

@article{Ahmed2017SamplingA,
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	title = {{Sampling Based Approaches for Minimizing Regret in Uncertain {Markov} Decision Processes}},
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	year = {2017}
}

@article{Delage2009Percentile,
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	journal = {Operations Research},
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	pages = {203--213},
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	volume = {58},
	year = {2010}
}

@article{goyal2020BeyondR,
      title={Robust Markov Decision Process: Beyond Rectangularity},
      author={Vineet Goyal and Julien Grand-Clement},
      year={2020},
      journal={Preprint arXiv:1811.00215}
}

@article{Roorda2004CoherentAM,
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  pages = {589--612}
}

@INPROCEEDINGS{Prashant2014PG,
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    title = {Policy Gradients for {CVaR}-Constrained {MDP}s},
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    year = {2014}
}

@article{iyengar2005RMDP,
 author = {Garud N. Iyengar},
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 pages = {257--280},
 publisher = {INFORMS},
 title = {Robust Dynamic Programming},
 volume = {30},
 year = {2005}
}

@article{Wiesemann2013RMDP,
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 title = {Robust {Markov} Decision Processes},
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 number = {1},
 year = {2013},
 pages = {153--183},
 publisher = {INFORMS},
}

@INPROCEEDINGS{Thrun93issuesin,
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@book{putterman1994DP,
author = {Puterman, Martin L.},
title = {Markov Decision Processes: Discrete Stochastic Dynamic Programming},
year = {2005},
publisher = {John Wiley and Sons, Inc.},
edition = {2nd},
}
@misc{rakhsha2020policy,
      title={Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning}, 
      author={Amin Rakhsha and Goran Radanovic and Rati Devidze and Xiaojin Zhu and Adish Singla},
      year={2020},
      eprint={2003.12909},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
@article{pearmutter1994fast,
author = {Pearlmutter, Barak A.},
title = {Fast Exact Multiplication by the {H}essian},
year = {1994},
issue_date = {Jan. 1994},
publisher = {MIT Press},
address = {Cambridge, MA, USA},
volume = {6},
number = {1},
issn = {0899-7667},
url = {https://doi.org/10.1162/neco.1994.6.1.147},
doi = {10.1162/neco.1994.6.1.147},
abstract = {Just storing the Hessian H (the matrix of second derivatives
δ2E/δwiδ
wj of the error E with respect to each
pair of weights) of a large neural network is difficult. Since a
common use of a large matrix like H is to compute its product with
various vectors, we derive a technique that directly calculates Hv,
where v is an arbitrary vector. To calculate Hv, we first define a
differential operator Rv{f(w)} =
(δ/δr)f(w + rv)|r=0, note that
Rv{∇w} = Hv and Rv{w} =
v, and then apply Rv{·} to the equations
used to compute ∇w. The result is an exact and
numerically stable procedure for computing Hv, which takes about as
much computation, and is about as local, as a gradient evaluation.
We then apply the technique to a one pass gradient calculation
algorithm (backpropagation), a relaxation gradient calculation
algorithm (recurrent backpropagation), and two stochastic gradient
calculation algorithms (Boltzmann machines and weight
perturbation). Finally, we show that this technique can be used at
the heart of many iterative techniques for computing various
properties of H, obviating any need to calculate the full
Hessian.},
journal = {Neural Comput.},
month = jan,
pages = {147–160},
numpages = {14}
}
@misc{diakonikolas2019recent,
      title={Recent Advances in Algorithmic High-Dimensional Robust Statistics}, 
      author={Ilias Diakonikolas and Daniel M. Kane},
      year={2019},
      eprint={1911.05911},
      archivePrefix={arXiv},
      primaryClass={cs.DS}
}
@misc{broderick2021automatic,
      title={An Automatic Finite-Sample Robustness Metric: Can Dropping a Little Data Change Conclusions?}, 
      author={Tamara Broderick and Ryan Giordano and Rachael Meager},
      year={2021},
      eprint={2011.14999},
      archivePrefix={arXiv},
      primaryClass={stat.ME}
}
@InProceedings{riedmiller2005fqe,
author="Riedmiller, Martin",
editor="Gama, Jo{\~a}o
and Camacho, Rui
and Brazdil, Pavel B.
and Jorge, Al{\'i}pio M{\'a}rio
and Torgo, Lu{\'i}s",
title="Neural Fitted Q Iteration -- First Experiences with a Data Efficient Neural Reinforcement Learning Method",
booktitle="Machine Learning: ECML 2005",
year="2005",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="317--328",
abstract="This paper introduces NFQ, an algorithm for efficient and effective training of a Q-value function represented by a multi-layer perceptron. Based on the principle of storing and reusing transition experiences, a model-free, neural network based Reinforcement Learning algorithm is proposed. The method is evaluated on three benchmark problems. It is shown empirically, that reasonably few interactions with the plant are needed to generate control policies of high quality.",
isbn="978-3-540-31692-3"
}


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      author={Mehrdad Farajtabar and Yinlam Chow and Mohammad Ghavamzadeh},
      year={2018},
      journal={arXiv 1802.03493}
}

@article{Chen2019adversarial,
author = {Chen, Tong and Liu, Jiqiang and Xiang, Yingxiao and Niu, Wenjia and Tong, Endong and Han, Zhen},
year = {2019},
month = {12},
pages = {},
title = {Adversarial attack and defense in reinforcement learning-from AI security view},
volume = {2},
journal = {Cybersecurity},
doi = {10.1186/s42400-019-0027-x}
}
@misc{ma2019policy,
      title={Policy Poisoning in Batch Reinforcement Learning and Control}, 
      author={Yuzhe Ma and Xuezhou Zhang and Wen Sun and Xiaojin Zhu},
      year={2019},
      eprint={1910.05821},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
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  author={Ernst, Damien and Stan, Guy-Bart and Goncalves, Jorge and Wehenkel, Louis},
  booktitle={Proceedings of the 45th IEEE Conference on Decision and Control}, 
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  year={2006},
  volume={},
  number={},
  pages={667-672},
  doi={10.1109/CDC.2006.377527}}



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	booktitle = {Thirty First AAAI Conference on Artificial Intelligence},
	pages = {4545--4551},
}

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}

@inproceedings{Petrik2014RAAM,
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	year = {2014}
}


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	year = {2017},
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	pages = {444--450},
}
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year = {2002},
pages = {3-33},
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@article{Teh2006HDP,
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volume = {101},
number = {476},
pages = {1566-1581},
year  = {2006},
publisher = {Taylor \& Francis},

}
@InProceedings{Pang2018FastBellman,
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  author = 	 {Ho, Chin Pang and Petrik, Marek and Wiesemann, Wolfram},
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  pages = 	 {1979--1988},
  year = 	 {2018},
  editor = 	 {Dy, Jennifer and Krause, Andreas},
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}
@phdthesis{letallec2007Robust,
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  author={Yann Le Tallec},
  year={2007},
  school = {MIT}
}

@inproceedings{Ho2018FastBellman,
	author = {Ho, Chin Pang and Petrik, Marek and Wiesemann, Wolfram},
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	title = {Fast {Bellman} Updates for Robust {MDPs}},
	year = {2018}
}
@article{Nilim2005RobustControl,
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 number = {5},
 year = {2005},
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 publisher = {INFORMS},
}
@inproceedings{Chow2014CVAR,
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 title = {Algorithms for {CVaR} Optimization in {MDPs}},
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 year = {2014},
 location = {Montreal, Canada},
 pages = {3509--3517},
 publisher = {MIT Press},
}

@Article{boda2006DynamicRisk,
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  year=2006,
  volume={63},
  number={1},
  pages={169-186},
}

@article{Buerle2011MarkovDP,
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  author={Nicole B{\"a}uerle and Jonathan Ott},
  journal={Mathematical Methods of Operations Research},
  year={2011},
  volume={74},
  pages={361-379}
}
@article{Boucheron2009ABM,
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  author={S. Boucheron and {\'E}. Gassiat},
  journal={Electronic Journal of Statistics},
  year={2009},
  volume={3},
  pages={114-148}
}



@inproceedings{Lim2013RMDP,
author = {Lim, Shiau Hong and Xu, Huan and Mannor, Shie},
title = {Reinforcement Learning in Robust Markov Decision Processes},
year = {2013},
booktitle = {International Conference on Neural Information Processing Systems},
volume={1},
pages = {701–709},
numpages = {9},
series = {NIPS'13}
}



@article{iancu2011TightApprox,
author = {Iancu, Dan and Petrik, Marek and Subramanian, Dharmashankar},
year = {2011},
title = {Tight Approximations of Dynamic Risk Measures},
volume = {40},
journal = {Mathematics of Operations Research},
}

@inproceedings{Tirizoni2018PolicyC,
author = {Tirinzoni, Andrea and Chen, Xiangli and Petrik, Marek and Ziebart, Brian D.},
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year = {2018},
booktitle = {International Conference on Neural Information Processing Systems},
pages = {8953–8963}
}


@article{Mannor2016KRect,
author = {Mannor, Shie and Mebel, Ofir and Xu, Huan},
title = {Robust {MDPs} with {K}-Rectangular Uncertainty},
year = {2016},
publisher = {INFORMS},
address = {Linthicum, MD, USA},
volume = {41},
number = {4},
journal = {Mathematics of Operations Research},
pages = {1484–1509},
}


@article{goyal2020robust,
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      author={Vineet Goyal and Julien Grand-Clement},
      year={2020},
      journal={Preprint arXiv:1811.00215}
}

@ARTICLE{Rockafellar2000optimizationCVAR,
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    title = {Optimization of Conditional Value-at-Risk},
    journal = {Journal of Risk},
    year = {2000},
    volume = {2},
    pages = {21--41}
}



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author = {Thomas, Philip S. and Teocharous, Georgios and Ghavamzadeh, Mohammad},
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year = {2015}
}

@article{Ben-Tal2019SoftRobust,
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 volume = {58},
 year = {2010}
}



@article{Xu2012DistributionallyRobust,
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 publisher = {INFORMS},
 title = {Distributionally Robust Markov Decision Processes},
 volume = {37},
 year = {2012}
}


@inproceedings{Derman2018Soft-RobustAC,
author = {Derman, Esther and Mankowitz, Daniel and Mann, Timothy A and Mannor, Shie},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
title = {{Soft-Robust Actor-Critic Policy-Gradient}},
year = {2018}
}

@inproceedings{Xu2006Robustnesstradeoff,
author = {Xu, Huan and Mannor, Shie},
booktitle = { International Conference on Neural Information Processing Systems
},
title = {The robustness-performance trade-off in {Markov} decision processes},
year = {2006}
}


@article{Chen2012RobustPO,
author = {Chen, Katherine and Bowling, Michael},
journal = {International Conference on Neural Information Processing Systems},
pages = {2069--2077},
title = {{Tractable objectives for robust policy optimization}},
volume = {3},
year = {2012}
}
@article{Mankowitz2020RobustRL,
  title={Robust Reinforcement Learning for Continuous Control with Model Misspecification},
  author={Daniel J. Mankowitz and Nir Levine and Rae Ung Jeong and Abbas Abdolmaleki and Jost Tobias Springenberg and Timothy Mann and Todd Hester and Martin A. Riedmiller},
  journal={ArXiv},
  year={2020},
  volume={abs/1906.07516}
}

@inproceedings{Strehllittman2004Exploration,
author = {Strehl, Alexander L and Littman, Michael L},
booktitle = {International Conference of Machine Learning},
title = {{Exploration via Model-based Interval Estimation}},
year = {2004}
}
@inproceedings{tamar15PG,
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 booktitle = {International Conference on Neural Information Processing Systems},
 pages = {1468--1476},
 title = {Policy Gradient for Coherent Risk Measures},
 volume = {28},
 year = {2015}
}


@inproceedings{Tamar2014Scaling,
    author = {Tamar, Aviv and Mannor, Shie and Xu, Huan},
    title = {Scaling up Robust {MDPs} Using Function Approximation},
    year = {2014},
    booktitle = {Proceedings of the 31st International Conference on Machine Learning},
    pages = {181–189}  }



@phdthesis{Bagnell2004LearningDR,
  title={Learning decisions: robustness, uncertainty, and approximation},
  author={J. Bagnell},
  year={2004},
  school = {CMU}
}

@inproceedings{Strehl2004Exploration,
	author = {Strehl, Alexander L and Littman, Michael L},
	booktitle = {Proceedings of the 21st International Machine Learning Conference},
	title = {{Exploration via Model-based Interval Estimation}},
	year = {2004}
}

@inproceedings{Gottesman2019Parametric,
	author = {Gottesman, Omer and Liu, Yao and Sussex, Scott and Brunskill, Emma and Doshi-Velez, Finale},
	booktitle = {Proceedings of the 36th International Conference on Machine Learning},
	eprint = {1905.05787},
	pages = {4236--4245},
	title = {{Combining parametric and non-parametric models for off-policy evaluation}},
	year = {2019}
}

@book{Gelman2014BayesianData,
	author = {Gelman, Andrew and Carlin, John B and Stern, Hal S and Rubin, Donald B},
	booktitle = {Chapman Texts in Statistical Science Series},
	publisher = {Chapman and Hall/CRC},
	title = {{Bayesian} Data Analysis},
	year = {2014}
}
@book{Seeger2004Gaussian,
	author = {Rasmussen, Carl Edward and Williams, Christopher},
	pmid = {15112367},
	title = {{Gaussian processes for machine learning.}},
	publisher = {Springer},
	year = {2004}
}
@article{calamai1987ProjectGD,
author = {Calamai, Paul H. and Mor\'{e}, Jorge J.},
title = {Projected Gradient Methods for Linearly Constrained Problems},
year = {1987},
publisher = {Springer-Verlag},
volume = {39},
number = {1},
journal = {Math. Program.},
pages = {93–116}
}
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@misc{koh2018stronger,
      title={Stronger Data Poisoning Attacks Break Data Sanitization Defenses}, 
      author={Pang Wei Koh and Jacob Steinhardt and Percy Liang},
      year={2018},
      eprint={1811.00741},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}

@misc{koh2020understanding,
      title={Understanding Black-box Predictions via Influence Functions}, 
      author={Pang Wei Koh and Percy Liang},
      year={2020},
      eprint={1703.04730},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}


@inproceedings{Thomas2015Highconfidence,
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title = {High Confidence Off-Policy Evaluation},
year = {2015},
isbn = {0262511290},
publisher = {AAAI Press},
booktitle = {Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence},
pages = {3000–3006},
numpages = {7},
location = {Austin, Texas},
series = {AAAI'15}
}
@article{Wiesemann2013Pessimistic,
author = {Wiesemann, Wolfram and Tsoukalas, Angelos and Kleniati, Polyxeni-Margarita and Rustem, Berç},
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number = {1},
pages = {353-380},
year = {2013}
}


@InProceedings{jiang16doublyrobust, title = {Doubly Robust Off-policy Value Evaluation for Reinforcement Learning}, author = {Nan Jiang and Lihong Li}, booktitle = {Proceedings of The 33rd International Conference on Machine Learning}, pages = {652--661}, year = {2016}, editor = {Maria Florina Balcan and Kilian Q. Weinberger}, volume = {48}, series = {Proceedings of Machine Learning Research}, publisher = {PMLR} }


misc{koh2018stronger,
      title={Stronger Data Poisoning Attacks Break Data Sanitization Defenses}, 
      author={Pang Wei Koh and Jacob Steinhardt and Percy Liang},
      year={2018},
      eprint={1811.00741},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}

@inproceedings{Thomas15highconfidence,
author = {Thomas, Philip S. and Theocharous, Georgios and Ghavamzadeh, Mohammad},
title = {High Confidence Off-Policy Evaluation},
year = {2015},
isbn = {0262511290},
publisher = {AAAI Press},
pages = {3000–3006},
numpages = {7},
location = {Austin, Texas},
series = {AAAI'15}
}

@InProceedings{thomas15policyimprovement, title = {High Confidence Policy Improvement}, author = {Thomas, Philip and Theocharous, Georgios and Ghavamzadeh, Mohammad}, booktitle = {Proceedings of the 32nd International Conference on Machine Learning}, pages = {2380--2388}, year = {2015}, editor = {Bach, Francis and Blei, David}, volume = {37}, series = {Proceedings of Machine Learning Research}, address = {Lille, France}, month = {07--09 Jul}, publisher = {PMLR}}







@misc{hanna2019importancesampling,
      title={Importance Sampling Policy Evaluation with an Estimated Behavior Policy}, 
      author={Josiah P. Hanna and Scott Niekum and Peter Stone},
      year={2019},
      eprint={1806.01347},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}


@misc{hanna2018bootstrapping,
      title={Bootstrapping with Models: Confidence Intervals for Off-Policy Evaluation}, 
      author={Josiah P. Hanna and Peter Stone and Scott Niekum},
      year={2018},
      eprint={1606.06126},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}

misc{gottesman2018evaluating,
      title={Evaluating Reinforcement Learning Algorithms in Observational Health Settings}, 
      author={Omer Gottesman and Fredrik Johansson and Joshua Meier and Jack Dent and Donghun Lee and Srivatsan Srinivasan and Linying Zhang and Yi Ding and David Wihl and Xuefeng Peng and Jiayu Yao and Isaac Lage and Christopher Mosch and Li-wei H. Lehman and Matthieu Komorowski and Matthieu Komorowski and Aldo Faisal and Leo Anthony Celi and David Sontag and Finale Doshi-Velez},
      year={2018},
      eprint={1805.12298},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{voloshin2020empirical,
      title={Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning}, 
      author={Cameron Voloshin and Hoang M. Le and Nan Jiang and Yisong Yue},
      year={2020},
      eprint={1911.06854},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{thomas2016dataefficient,
      title={Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning}, 
      author={Philip S. Thomas and Emma Brunskill},
      year={2016},
      eprint={1604.00923},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
@misc{gottesman2020interpretable,
      title={Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions}, 
      author={Omer Gottesman and Joseph Futoma and Yao Liu and Sonali Parbhoo and Leo Anthony Celi and Emma Brunskill and Finale Doshi-Velez},
      year={2020},
      eprint={2002.03478},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
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}
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author = {Rubinstein, Reuven Y.},
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year = {1981},
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@inproceedings{Zhang2020AdaptiveRA,
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  author={Xuezhou Zhang and Yuzhe Ma and A. Singla and Xiaojin Zhu},
  booktitle={ICML},
  year={2020}
}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


@misc{lobo2021softrobust,
      title={Soft-Robust Algorithms for Batch Reinforcement Learning}, 
      author={Elita A. Lobo and Mohammad Ghavamzadeh and Marek Petrik},
      year={2021},
      eprint={2011.14495},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
@misc{koh2020understanding,
      title={Understanding Black-box Predictions via Influence Functions}, 
      author={Pang Wei Koh and Percy Liang},
      year={2020},
      eprint={1703.04730},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}
@misc{koh2020understanding,
      title={Understanding Black-box Predictions via Influence Functions}, 
      author={Pang Wei Koh and Percy Liang},
      year={2020},
      eprint={1703.04730},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}
@misc{gottesman2020interpretable,
      title={Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions}, 
      author={Omer Gottesman and Joseph Futoma and Yao Liu and Sonali Parbhoo and Leo Anthony Celi and Emma Brunskill and Finale Doshi-Velez},
      year={2020},
      eprint={2002.03478},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
@misc{ma2019policy,
      title={Policy Poisoning in Batch Reinforcement Learning and Control}, 
      author={Yuzhe Ma and Xuezhou Zhang and Wen Sun and Xiaojin Zhu},
      year={2019},
      eprint={1910.05821},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
@misc{ma2019policy,
      title={Policy Poisoning in Batch Reinforcement Learning and Control}, 
      author={Yuzhe Ma and Xuezhou Zhang and Wen Sun and Xiaojin Zhu},
      year={2019},
      eprint={1910.05821},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
@misc{koh2021stronger,
      title={Stronger Data Poisoning Attacks Break Data Sanitization Defenses}, 
      author={Pang Wei Koh and Jacob Steinhardt and Percy Liang},
      year={2021},
      eprint={1811.00741},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}
@misc{koh2021stronger,
      title={Stronger Data Poisoning Attacks Break Data Sanitization Defenses}, 
      author={Pang Wei Koh and Jacob Steinhardt and Percy Liang},
      year={2021},
      eprint={1811.00741},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}
@misc{cohen2020detecting,
      title={Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors}, 
      author={Gilad Cohen and Guillermo Sapiro and Raja Giryes},
      year={2020},
      eprint={1909.06872},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
@misc{fang2020influence,
      title={Influence Function based Data Poisoning Attacks to Top-N Recommender Systems}, 
      author={Minghong Fang and Neil Zhenqiang Gong and Jia Liu},
      year={2020},
      eprint={2002.08025},
      archivePrefix={arXiv},
      primaryClass={cs.CR}
}
@misc{zhang2021corruptionrobust,
      title={Corruption-Robust Offline Reinforcement Learning}, 
      author={Xuezhou Zhang and Yiding Chen and Jerry Zhu and Wen Sun},
      year={2021},
      eprint={2106.06630},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
@misc{zhang2019online,
      title={Online Data Poisoning Attack}, 
      author={Xuezhou Zhang and Xiaojin Zhu and Laurent Lessard},
      year={2019},
      eprint={1903.01666},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
@misc{steinhardt2017certified,
      title={Certified Defenses for Data Poisoning Attacks}, 
      author={Jacob Steinhardt and Pang Wei Koh and Percy Liang},
      year={2017},
      eprint={1706.03691},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
@misc{gleave2021adversarial,
      title={Adversarial Policies: Attacking Deep Reinforcement Learning}, 
      author={Adam Gleave and Michael Dennis and Cody Wild and Neel Kant and Sergey Levine and Stuart Russell},
      year={2021},
      eprint={1905.10615},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
@article{Chen2019AdversarialAA,
  title={Adversarial attack and defense in reinforcement learning-from AI security view},
  author={Tong Chen and Jiqiang Liu and Yingxiao Xiang and Wenjia Niu and Endong Tong and Zhen Han},
  journal={Cybersecurity},
  year={2019},
  volume={2},
  pages={1-22}
}
@misc{gleave2021adversarial,
      title={Adversarial Policies: Attacking Deep Reinforcement Learning}, 
      author={Adam Gleave and Michael Dennis and Cody Wild and Neel Kant and Sergey Levine and Stuart Russell},
      year={2021},
      eprint={1905.10615},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
@misc{lin2019tactics,
      title={Tactics of Adversarial Attack on Deep Reinforcement Learning Agents}, 
      author={Yen-Chen Lin and Zhang-Wei Hong and Yuan-Hong Liao and Meng-Li Shih and Ming-Yu Liu and Min Sun},
      year={2019},
      eprint={1703.06748},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
@inbook{10.1145/3447548.3467335,
author = {Wu, Chenwang and Lian, Defu and Ge, Yong and Zhu, Zhihao and Chen, Enhong},
title = {Triple Adversarial Learning for Influence Based Poisoning Attack in Recommender Systems},
year = {2021},
isbn = {9781450383325},
publisher = {Association for Computing Machinery},
booktitle = {Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining},
pages = {1830–1840},
numpages = {11}
}
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isbn="978-1-4419-9863-7",
doi="10.1007/978-1-4419-9863-7_1235"
}


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numpages = {8},
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series = {NIPS'08}
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year = {2019},
publisher = {Curran Associates, Inc.},
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}

@misc{tensorflow2015-whitepaper,
title={ {TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems},
url={https://www.tensorflow.org/},
note={Software available from tensorflow.org},
author={
    Mart\'{i}n~Abadi and
    Ashish~Agarwal and
    Paul~Barham and
    Eugene~Brevdo and
    Zhifeng~Chen and
    Craig~Citro and
    Greg~S.~Corrado and
    Andy~Davis and
    Jeffrey~Dean and
    Matthieu~Devin and
    Sanjay~Ghemawat and
    Ian~Goodfellow and
    Andrew~Harp and
    Geoffrey~Irving and
    Michael~Isard and
    Yangqing Jia and
    Rafal~Jozefowicz and
    Lukasz~Kaiser and
    Manjunath~Kudlur and
    Josh~Levenberg and
    Dandelion~Man\'{e} and
    Rajat~Monga and
    Sherry~Moore and
    Derek~Murray and
    Chris~Olah and
    Mike~Schuster and
    Jonathon~Shlens and
    Benoit~Steiner and
    Ilya~Sutskever and
    Kunal~Talwar and
    Paul~Tucker and
    Vincent~Vanhoucke and
    Vijay~Vasudevan and
    Fernanda~Vi\'{e}gas and
    Oriol~Vinyals and
    Pete~Warden and
    Martin~Wattenberg and
    Martin~Wicke and
    Yuan~Yu and
    Xiaoqiang~Zheng},
  year={2015},
}
