K.J Åström (1965):
Optimal control of Markov processes with incomplete state information.
Journal of Mathematical Analysis and Applications 10(1),
pp. 174–205,
doi:10.1016/0022-247x(65)90154-x.
John Baez & Jason Erbele (2015):
Categories in control.
Theory and applications of categories 30(24),
pp. 836–881.
ArXiv:1405.6881.
Wolfram Barfuss (2019):
Learning dynamics and decision paradigms in social-ecological dilemmas.
Humboldt-Universität zu Berlin,
doi:10.18452/20127.
Richard Bellman (1957):
Dynamic Programming.
Princeton University Press,
doi:10.1515/9781400835386.
Dimitri P. Bertsekas (2007):
Dynamic Programming and Optimal Control, Vol. II,
3rd edition.
Athena Scientific.
Joe Bolt, Jules Hedges & Philipp Zahn (2019):
Bayesian open games.
ArXiv:1910.03656.
Forthcoming in Compositionality.
Nicola Botta, Cezar Ionescu & Edwin C. Brady (2013):
Sequential decision problems, dependently-typed solutions.
In: Joint Proceedings of the MathUI, OpenMath, PLMMS and ThEdu Workshops and Work in Progress at CICM, Bath, UK,
CEUR Workshop Proceedings 1010.
CEUR-WS.org.
Available at http://ceur-ws.org/Vol-1010/paper-06.pdf.
Matteo Capucci & Bruno Gavranovi\'c (2022):
Actegories for the working amthematician.
ArXiv:2203.16351.
ArXiv:2203.16351.
Matteo Capucci, Bruno Gavranovi\'c, Jules Hedges & Eigil Fjeldgren Rischel (2022):
Towards foundations of categorical cybernetics.
Electronic Proceedings in Theoretical Computer Science 372,
pp. 235–248,
doi:10.4204/eptcs.372.17.
Matteo Capucci, Neil Ghani, Jérémy Ledent & Fredrik Nordvall Forsberg (2022):
Translating extensive form games to open games with agency.
Electronic Proceedings in Theoretical Computer Science 372,
pp. 221–234,
doi:10.4204/eptcs.372.16.
Bryce Clarke, Derek Elkins, Jeremy Gibbons, Fosco Loregian, Bartosz Milewski, Emily Pillmore & Mario Román (2020):
Profunctor optics: A categorical update.
ArXiv:2001.07488.
G.S.H. Cruttwell, Bruno Gavranovi\'c, Neil Ghani, Paul Wilson & Fabio Zanasi (2021):
Categorical foundations of gradient-based learning.
ArXiv:2103.01931.
Nicolas Eschenbaum, Filip Mellgren & Philipp Zahn (2022):
Robust algorithmic collusion.
ArXiv:2201.00345.
Frank M. V. Feys, Helle Hvid Hansen & Lawrence S. Moss (2018):
Long-Term Values in Markov Decision Processes, (Co)Algebraically.
In: Corina Cîrstea: Coalgebraic Methods in Computer Science 11202.
Springer International Publishing,
Cham,
pp. 78–99,
doi:10.1007/978-3-030-00389-0_6.
Available at http://link.springer.com/10.1007/978-3-030-00389-0_6.
Series Title: Lecture Notes in Computer Science.
Bernard Friedland (1985):
Control Systems Design.
McGraw-Hill Companies.
Tobias Fritz (2009):
Convex spaces I: Definitions and examples.
ArXiv:0903.5522.
Tobias Fritz (2020):
A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics.
Advances in Mathematics 370,
pp. 107239,
doi:10.1016/j.aim.2020.107239.
ArXiv:1908.07021.
Tobias Fritz & Paolo Perrone (2019):
A probability monad as the colimit of spaces of finite samples.
Theory and applications of categories 34(7),
pp. 170–220.
ArXiv:1712.05363.
Neil Ghani, Jules Hedges, Viktor Winschel & Philipp Zahn (2018):
Compositional game theory.
In: Proceedings of Logic in Computer Science (LiCS) 2018.
ACM,
pp. 472–481,
doi:10.1145/3209108.3209165.
Michèlle Giry (1982):
A categorical approach to probability theory.
In: Lecture Notes in Mathematics.
Springer Berlin Heidelberg,
pp. 68–85,
doi:10.1007/bfb0092872.
Jules Hedges (2017):
Coherence for lenses and open games.
ArXiv:1704.02230.
Jules Hedges (2018):
Morphisms of open games.
In: Proceedings of MFPS 2018 341.
Elsevier BV,
pp. 151–177,
doi:10.1016/j.entcs.2018.11.008.
L. P. Kaelbling, M. L. Littman & A. W. Moore (1996):
Reinforcement Learning: A Survey.
Journal of Artificial Intelligence Research 4,
pp. 237–285,
doi:10.1613/jair.301.
Anders Kock (1971):
Closed categories generated by commutative monads.
Journal of the Australian Mathematical Society 12(4),
pp. 405–424,
doi:10.1017/s1446788700010272.
Lars Ljungqvist & Thomas Sargent (2004):
Recursive macroeconomic theory.
MIT Press.
Fosco Loregian (2021):
Coend calculus.
Cambridge University Press.
ArXiv:1501.02503.
Sagar Mody & Thomas Steffen (2015):
Optimal charging of electric vehicles using a stochastic dynamic programming model and price prediction.
SAE International Journal of Passenger Cars - Electronic and Electrical Systems 8(2),
pp. 379–393,
doi:10.4271/2015-01-0302.
David Jaz Myers (2021):
Double Categories of Open Dynamical Systems (Extended Abstract).
Electronic Proceedings in Theoretical Computer Science 333,
pp. 154–167,
doi:10.4204/eptcs.333.11.
Available at http://arxiv.org/abs/2005.05956v2.
Martin L. Puterman (2005):
Markov decision processes: discrete stochastic dynamic programming.
Wiley series in probability and statistics.
Wiley-Interscience,
Hoboken, NJ.
Mitchell Riley (2018):
Categories of Optics.
ArXiv:1809.00738.
John Rust (1996):
Numerical dynamic programming in economics.
In: Handbook of Computational Economics.
Amsterdam: Elsevier,
pp. 619–729,
doi:10.1016/s1574-0021(96)01016-7.
David I. Spivak (2020):
Generalized Lens Categories via functors C^ op-Cat.
ArXiv:1908.02202.
Kirk Stirtz (2015):
Categorical probability theory.
ArXiv:1406.6030.
Nancy Stokey, Robert Lucas & Edward Prescott (1989):
Recursive methods in economic dynamics.
Harvard University Press,
doi:10.2307/j.ctvjnrt76.
Richard S. Sutton & Andrew G. Barto (2020):
Reinforcement Learning: An Introduction.
MIT Press.
T. \'Swirszcz (1974):
Monadic functors and convexity.
Bulletin de l'Acaédemie Polonaise des Sciences 22(1).
Pietro Vertechi (2022):
Dependent optics.
ArXiv:2204.09547.
Christopher J. C. H. Watkins & Peter Dayan (1992):
Q-learning.
Machine Learning 8(3-4),
pp. 279–292,
doi:10.1007/bf00992698.
eptcs@eptcs.org