References

For the cured R-quarto material see https://github.com/SaulDiazInfante/intro-quarto-unison-2024.

[1]

D.P. Bertsekas, Dynamic programming and optimal control. Vol. I, Third, Athena Scientific, Belmont, MA, 2005.

[2]

E. Bilgin, Mastering reinforcement learning with python: Build next-generation, self-learning models using reinforcement learning techniques and best practices, Packt Publishing, 2020.

[3]

P. Brandimarte, Numerical methods in finance and economics: A MATLAB-based introduction, 2nd ed., John Wiley & Sons, Hoboken, New Jersey, 2013.

[4]

S.L. Brunton, J.N. Kutz, Data-driven science and engineering, Cambridge University Press, Cambridge, 2019.

[5]

T. Decroos, L. Bransen, J.V. Haaren, J. Davis, VAEP: An Objective Approach to Valuing On-the-Ball Actions in Soccer (Extended Abstract), Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. (2020) 4696–4700.

[6]

D. Levhari, L.J. Mirman, The great fish war: An example using a dynamic cournot-nash solution, Essays in the Economics of Renewable Resources, LJ Mirman and DF Spulber (Eds.), North-Holland. (1982) 243–258.

[7]

J. Rust, Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher, Econometrica. 55 (1987) 999.

[8]

J. Stachurski., Dynamic programming volume 1, GitHub Repository. (2024).

[9]

R.S. Sutton, A.G. Barto, Reinforcement learning: An introduction, Second, MIT Press, Cambridge, MA, 2018.

[10]

C. Szepesvári, Algorithms for reinforcement learning, Springer, Cham, 2022.