references.bib

---
---

@article{Hall1968,
  title = {Optimal Timing of Irrigation},
  volume = {94},
  ISSN = {2690-3296},
  url = {http://dx.doi.org/10.1061/JRCEA4.0000569},
  DOI = {10.1061/jrcea4.0000569},
  number = {2},
  journal = {Journal of the Irrigation and Drainage Division},
  publisher = {American Society of Civil Engineers (ASCE)},
  author = {Hall,  Warren A. and Butcher,  William S.},
  year = {1968},
  month = jun,
  pages = {267–275}
}

@article{kraft1988,
  title={A software package for sequential quadratic programming},
  author={Kraft, Dieter},
  journal={DFVLR Obersfaffeheim Report},
  volume={88},
  number={28},
  pages={1--20},
  year={1988}
}

@article{holland1992genetic,
  title={Genetic algorithms},
  author={Holland, John H},
  journal={Scientific american},
  volume={267},
  number={1},
  pages={66--73},
  year={1992},
  publisher={JSTOR}
}

@article{kirkpatrick1983optimization,
  title={Optimization by simulated annealing},
  author={Kirkpatrick, Scott and Gelatt Jr, C Daniel and Vecchi, Mario P},
  journal={science},
  volume={220},
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  pages={671--680},
  year={1983},
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}

@inproceedings{kennedy1995particle,
  title={Particle swarm optimization},
  author={Kennedy, James and Eberhart, Russell},
  booktitle={Proceedings of ICNN'95-International Conference on Neural Networks},
  volume={4},
  pages={1942--1948},
  year={1995},
  organization={IEEE}
}

@article{Iskhakov2020,
  title = {Machine learning and structural econometrics: contrasts and synergies},
  volume = {23},
  ISSN = {1368-423X},
  url = {http://dx.doi.org/10.1093/ectj/utaa019},
  DOI = {10.1093/ectj/utaa019},
  number = {3},
  journal = {The Econometrics Journal},
  publisher = {Oxford University Press (OUP)},
  author = {Iskhakov,  Fedor and Rust,  John and Schjerning,  Bertel},
  year = {2020},
  month = aug,
  pages = {S81–S124}
}

@book{ortega_rheinboldt_1970,
  author    = {J. M. Ortega and W. C. Rheinboldt},
  title     = {Iterative Solution of Nonlinear Equations in Several Variables},
  year      = {1970},
  publisher = {Academic Press},
  address   = {New York},
  series    = {Computer Science and Applied Mathematics},
}

@book{allgower_georg_1990,
  author    = {E. L. Allgower and K. Georg},
  title     = {Numerical Continuation Methods: An Introduction},
  year      = {1990},
  publisher = {Springer-Verlag},
  address   = {Berlin, Heidelberg},
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}
@book{arrow1958studies,
  title={Studies in linear and non-linear programming},
  author={Arrow, Kenneth J and Hurwicz, Leonid and Uzawa, Hirofumi},
  year={1958},
  publisher={Stanford University Press}
}

@inproceedings{goodfellow2014generative,
  title={Generative adversarial nets},
  author={Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
  booktitle={Advances in neural information processing systems},
  volume={27},
  year={2014}
}
@book{pontryagin1962mathematical,
  title={The Mathematical Theory of Optimal Processes},
  author={Pontryagin, Lev Semyonovich and Boltyanskii, Vladimir Grigor'evich and Gamkrelidze, Revaz Valerianovich and Mishchenko, Evgenii Frolovich},
  year={1962},
  publisher={Interscience Publishers}
}

@article{rumelhart1986learning,
  title={Learning representations by back-propagating errors},
  author={Rumelhart, David E and Hinton, Geoffrey E and Williams, Ronald J},
  journal={Nature},
  volume={323},
  number={6088},
  pages={533--536},
  year={1986},
  publisher={Nature Publishing Group}
}

@book{griewank1989automatic,
  title={On automatic differentiation},
  author={Griewank, Andreas},
  year={1989},
  publisher={Mathematical Programming: Recent Developments and Applications}
}

@article{lecun1988theoretical,
  title={A theoretical framework for back-propagation},
  author={LeCun, Yann},
  journal={Proceedings of the 1988 Connectionist Models Summer School},
  pages={21--28},
  year={1988},
  publisher={Morgan Kaufmann}
}
@inproceedings{Gravdahl1997,
  title = {Compressor surge control using a close-coupled valve and backstepping},
  url = {http://dx.doi.org/10.1109/ACC.1997.609673},
  DOI = {10.1109/acc.1997.609673},
  booktitle = {Proceedings of the 1997 American Control Conference (Cat. No.97CH36041)},
  publisher = {IEEE},
  author = {Gravdahl,  J.T. and Egeland,  O.},
  year = {1997},
  pages = {982–986 vol.2}
}

@book{Grancharova2012,
  title     = "Explicit nonlinear model predictive control",
  author    = "Grancharova, Alexandra Ivanova and Johansen, Tor Arne",
  publisher = "Springer",
  series    = "Lecture notes in control and information sciences",
  edition   =  2012,
  month     =  mar,
  year      =  2012,
  address   = "Berlin, Germany",
  copyright = "https://www.springernature.com/gp/researchers/text-and-data-mining",
  language  = "en"
}
@book{fiacco1983introduction,
  title={Introduction to Sensitivity and Stability Analysis in Nonlinear Programming},
  author={Fiacco, Anthony V.},
  year={1983},
  publisher={Academic Press}
}
@article{Sawaguchi2008,
  title = {A Model-Predictive Hypnosis Control System Under Total Intravenous Anesthesia},
  volume = {55},
  ISSN = {0018-9294},
  url = {http://dx.doi.org/10.1109/tbme.2008.915670},
  DOI = {10.1109/tbme.2008.915670},
  number = {3},
  journal = {IEEE Transactions on Biomedical Engineering},
  publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
  author = {Sawaguchi,  Y. and Furutani,  E. and Shirakami,  G. and Araki,  M. and Fukuda,  K.},
  year = {2008},
  month = mar,
  pages = {874–887}
}
@article{Adams2009,
  title = {Spending on new drug development1},
  volume = {19},
  ISSN = {1099-1050},
  url = {http://dx.doi.org/10.1002/hec.1454},
  DOI = {10.1002/hec.1454},
  number = {2},
  journal = {Health Economics},
  publisher = {Wiley},
  author = {Adams,  Christopher Paul and Brantner,  Van Vu},
  year = {2009},
  month = feb,
  pages = {130–141}
}
@book{Chang2010, 
  title={Monte Carlo Simulation for the Pharmaceutical Industry: Concepts, Algorithms, and Case Studies}, 
  ISBN={9780429152382}, 
  url={http://dx.doi.org/10.1201/EBK1439835920}, 
  DOI={10.1201/ebk1439835920}, 
  publisher={CRC Press}, 
  author={Chang, Mark}, 
  year={2010}, 
  month=sep 
}
@book{Conroy2013,
  title = {Decision Making in Natural Resource Management: A Structured,  Adaptive Approach: A Structured,  Adaptive Approach},
  ISBN = {9781118506196},
  url = {http://dx.doi.org/10.1002/9781118506196},
  DOI = {10.1002/9781118506196},
  publisher = {Wiley},
  author = {Conroy,  Michael J. and Peterson,  James T.},
  year = {2013},
  month = jan 
}
@book{Puterman1994,
  author    = {Martin L. Puterman},
  title     = {Markov Decision Processes: Discrete Stochastic Dynamic Programming},
  year      = {1994},
  publisher = {John Wiley \& Sons},
  address   = {New York},
  isbn      = {978-0-471-61977-3},
  note      = {First published in 1994},
}
@article{rust1987optimal,
    author = {John Rust},
    title = {Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher},
    journal = {Econometrica},
    volume = {55},
    number = {5},
    pages = {999-1033},
    year = {1987},
    publisher = {JSTOR}
}

@inproceedings{ziebart2008maximum,
    author = {Brian D. Ziebart and Andrew L. Maas and J. Andrew Bagnell and Anind K. Dey},
    title = {Maximum Entropy Inverse Reinforcement Learning},
    booktitle = {Proceedings of the 23rd AAAI Conference on Artificial Intelligence},
    year = {2008},
    pages = {1433-1438}
}
 
@article{haarnoja2017reinforcement,
    author = {Tuomas Haarnoja and Haoran Tang and Pieter Abbeel and Sergey Levine},
    title = {Reinforcement Learning with Deep Energy-Based Policies},
    journal = {Proceedings of the 34th International Conference on Machine Learning},
    year = {2017},
    pages = {1352-1361},
    volume = {70},
    publisher = {PMLR}
}
@article{levine2018reinforcement,
    author = {Sergey Levine and Aviral Kumar and George Tucker and Justin Fu},
    title = {Reinforcement Learning as a Framework for Control: A Survey},
    journal = {arXiv preprint arXiv:1806.04222},
    year = {2018}
}

@InProceedings{geist2019,
  title = 	 {A Theory of Regularized {M}arkov Decision Processes},
  author =       {Geist, Matthieu and Scherrer, Bruno and Pietquin, Olivier},
  booktitle = 	 {Proceedings of the 36th International Conference on Machine Learning},
  pages = 	 {2160--2169},
  year = 	 {2019},
  editor = 	 {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
  volume = 	 {97},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {09--15 Jun},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v97/geist19a/geist19a.pdf},
  url = 	 {https://proceedings.mlr.press/v97/geist19a.html},
  abstract = 	 {Many recent successful (deep) reinforcement learning algorithms make use of regularization, generally based on entropy or Kullback-Leibler divergence. We propose a general theory of regularized Markov Decision Processes that generalizes these approaches in two directions: we consider a larger class of regularizers, and we consider the general modified policy iteration approach, encompassing both policy iteration and value iteration. The core building blocks of this theory are a notion of regularized Bellman operator and the Legendre-Fenchel transform, a classical tool of convex optimization. This approach allows for error propagation analyses of general algorithmic schemes of which (possibly variants of) classical algorithms such as Trust Region Policy Optimization, Soft Q-learning, Stochastic Actor Critic or Dynamic Policy Programming are special cases. This also draws connections to proximal convex optimization, especially to Mirror Descent.}
}
@article{Bertsekas1983,
  title = {Distributed asynchronous computation of fixed points},
  volume = {27},
  ISSN = {1436-4646},
  url = {http://dx.doi.org/10.1007/BF02591967},
  DOI = {10.1007/bf02591967},
  number = {1},
  journal = {Mathematical Programming},
  publisher = {Springer Science and Business Media LLC},
  author = {Bertsekas,  Dimitri P.},
  year = {1983},
  month = sep,
  pages = {107–120}
}

@inproceedings{haarnoja2018soft,
  title={Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor},
  author={Haarnoja, Tuomas and Zhou, Aurick and Abbeel, Pieter and Levine, Sergey},
  booktitle={Proceedings of the 35th International Conference on Machine Learning (ICML)},
  pages={1861--1870},
  year={2018},
  organization={PMLR}
}

@misc{kortum1992value,
  author    = {Kortum, Samuel},
  title     = {Value Function Approximation in an Estimation Routine},
  year      = {1992},
  note      = {Manuscript, Boston University},
}
@inbook{Rust1996,
  title = {Chapter 14 Numerical dynamic programming in economics},
  ISSN = {1574-0021},
  url = {http://dx.doi.org/10.1016/S1574-0021(96)01016-7},
  DOI = {10.1016/s1574-0021(96)01016-7},
  booktitle = {Handbook of Computational Economics},
  publisher = {Elsevier},
  author = {Rust,  John},
  year = {1996},
  pages = {619–729}
}
@article{ernst2005tree,
  author    = {Ernst, Damien and Geurts, Pierre and Wehenkel, Louis},
  title     = {Tree-Based Batch Mode Reinforcement Learning},
  journal   = {Journal of Machine Learning Research},
  volume    = {6},
  pages     = {503--556},
  year      = {2005},
}
@inproceedings{riedmiller2005neural,
  author    = {Riedmiller, Martin},
  title     = {Neural Fitted Q Iteration – First Experiences with a Data Efficient Neural Reinforcement Learning Method},
  booktitle = {Proceedings of the 16th European Conference on Machine Learning (ECML)},
  pages     = {317--328},
  year      = {2005},
  publisher = {Springer},
  address   = {Berlin, Heidelberg},
}
@article{Ormoneit2002,
  volume = {49},
  ISSN = {0885-6125},
  url = {http://dx.doi.org/10.1023/A:1017928328829},
  DOI = {10.1023/a:1017928328829},
  number = {2/3},
  title = {Kernel-Based Reinforcement Learning},
  journal = {Machine Learning},
  publisher = {Springer Science and Business Media LLC},
  author = {Ormoneit,  Dirk and Sen,  Śaunak},
  year = {2002},
  pages = {161–178}
}
@article{ErnstGW05,
  author       = {Damien Ernst and
                  Pierre Geurts and
                  Louis Wehenkel},
  title        = {Tree-Based Batch Mode Reinforcement Learning},
  journal      = {J. Mach. Learn. Res.},
  volume       = {6},
  pages        = {503--556},
  year         = {2005},
  url          = {https://jmlr.org/papers/v6/ernst05a.html}
}
@article{Geurts2006,
  title = {Extremely randomized trees},
  volume = {63},
  ISSN = {1573-0565},
  url = {http://dx.doi.org/10.1007/s10994-006-6226-1},
  DOI = {10.1007/s10994-006-6226-1},
  number = {1},
  journal = {Machine Learning},
  publisher = {Springer Science and Business Media LLC},
  author = {Geurts,  Pierre and Ernst,  Damien and Wehenkel,  Louis},
  year = {2006},
  month = mar,
  pages = {3–42}
}
@inproceedings{Riedmiller05,
  author       = {Martin A. Riedmiller},
  editor       = {Jo{\~{a}}o Gama and
                  Rui Camacho and
                  Pavel Brazdil and
                  Al{\'{\i}}pio Jorge and
                  Lu{\'{\i}}s Torgo},
  title        = {Neural Fitted {Q} Iteration - First Experiences with a Data Efficient
                  Neural Reinforcement Learning Method},
  booktitle    = {Machine Learning: {ECML} 2005, 16th European Conference on Machine
                  Learning, Porto, Portugal, October 3-7, 2005, Proceedings},
  series       = {Lecture Notes in Computer Science},
  volume       = {3720},
  pages        = {317--328},
  publisher    = {Springer},
  year         = {2005},
  url          = {https://doi.org/10.1007/11564096\_32},
  doi          = {10.1007/11564096\_32}
}
@inproceedings{Gordon1995,
author = {Gordon, Geoffrey J.},
title = {Stable function approximation in dynamic programming},
year = {1995},
isbn = {1558603778},
publisher = {Morgan Kaufmann Publishers Inc.},
address = {San Francisco, CA, USA},
booktitle = {Proceedings of the Twelfth International Conference on International Conference on Machine Learning},
pages = {261–268},
numpages = {8},
location = {Tahoe City, California, USA},
series = {ICML'95}
}
@article{Hafner2011,
  title = {Reinforcement learning in feedback control: Challenges and benchmarks from technical process control},
  volume = {84},
  ISSN = {1573-0565},
  url = {http://dx.doi.org/10.1007/s10994-011-5235-x},
  DOI = {10.1007/s10994-011-5235-x},
  number = {1-2},
  journal = {Machine Learning},
  publisher = {Springer Science and Business Media LLC},
  author = {Hafner, Roland and Riedmiller, Martin},
  year = {2011},
  month = {feb},
  pages = {137-169}
}
@inproceedings{mnih2013atari,
  title={Playing Atari with Deep Reinforcement Learning},
  author={Mnih, Volodymyr and Kavukcuoglu, Koray and Silver, David and Rusu, Andrei A and Veness, Joel and Bellemare, Marc G and Graves, Alex and Riedmiller, Martin and Fidjeland, Andreas K and Ostrovski, Georg and others},
  booktitle={NIPS Deep Learning Workshop},
  year={2013}
}
@article{lillicrap2015continuous,
  title={Continuous Control with Deep Reinforcement Learning},
  author={Lillicrap, Timothy P and Hunt, Jonathan J and Pritzel, Alexander and Heess, Nicolas and Erez, Tom and Tassa, Yuval and Silver, David and Wierstra, Daan},
  journal={arXiv preprint arXiv:1509.02971},
  year={2015}
}
@inproceedings{fujimoto2018addressing,
  title={Addressing Function Approximation Error in Actor-Critic Methods},
  author={Fujimoto, Scott and Hoof, Herke and Meger, David},
  booktitle={International Conference on Machine Learning (ICML)},
  pages={1587--1596},
  year={2018}
}
@inproceedings{haarnoja2018soft,
  title={Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor},
  author={Haarnoja, Tuomas and Zhou, Aurick and Abbeel, Pieter and Levine, Sergey},
  booktitle={International Conference on Machine Learning (ICML)},
  pages={1861--1870},
  year={2018}
}
@inproceedings{ash2020warm,
  title={Warm-starting and Amortization in Continual Learning},
  author={Ash, Jordan T and Adams, Ryan P},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2020}
}
@inproceedings{Doro2023,
  author       = {Pierluca D'Oro and
                  Max Schwarzer and
                  Evgenii Nikishin and
                  Pierre-Luc Bacon and
                  Marc G. Bellemare and
                  Aaron C. Courville},
  title        = {Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio
                  Barrier},
  booktitle    = {The Eleventh International Conference on Learning Representations,
                  {ICLR} 2023, Kigali, Rwanda, May 1-5, 2023},
  publisher    = {OpenReview.net},
  year         = {2023}
}
@phdthesis{lin1992self,
  title={Self-improving reactive agents based on reinforcement learning, planning, and teaching},
  author={Lin, Long-Ji},
  year={1992},
  school={Carnegie Mellon University},
  address={Pittsburgh, PA, USA},
  note={Technical Report, CMU-CS-92-170}
}
@article{van2016deep,
  title={Deep Reinforcement Learning with Double Q-learning},
  author={Van Hasselt, Hado and Guez, Arthur and Silver, David},
  journal={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={30},
  number={1},
  year={2016},
  publisher={AAAI Press}
}