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== Lecturers and Seminarists ==

{| class="wikitable" style="text-align:center"
|-
|| Lecturer || [https://www.hse.ru/staff/anaumov Alexey Naumov] || [anaumov@hse.ru] || T924
|- 
|| Seminarist || [Ilya Levin] || [tg: @levensons] || T926
|-
|}

== About the course ==
This page contains materials for Mathematical Foundations of Reinforcement learning course in 2022/2023 year, optional one for 2nd year Master students of the Math of Machine Learning program (HSE and Skoltech).

== Grading == 
The final grade consists of 2 components (each is non-negative real number from 0 to 10, without any intermediate rounding) :
* O<sub>HW</sub> for the hometasks
* O<sub>Project</sub> for the course project
The formula for the final grade is 
* O<sub>Final</sub> = 0.6*O<sub>HW</sub> + 0.4*O<sub>Project</sub>
with the usual (arithmetical) rounding rule.

== Course materials ==
*[https://www.overleaf.com/read/kbzmvxdzbrxq '''Lectures and seminars notes''']
*[https://colab.research.google.com/drive/10qBq7Ot_1ZpnTeD11P5AnE8jFVj0OLXl?usp=sharing '''Notebook for the first seminar''']

== Recommended literature ==

* Sebastien Bubek, Nicolo Cesa-Bianchi. Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems. Chapter 2. http://sbubeck.com/SurveyBCB12.pdf
* Richard S. Sutton, Andrew G. Barto. Reinforcement Learning: An Introduction. Chapter 2. http://incompleteideas.net/book/the-book-2nd.html;
* Botao Hao et al. Bootstrapping Upper Confidence Bound. https://arxiv.org/abs/1906.05247
* Aleksandrs Slivkins. Introduction to Multi-Armed Bandits. https://arxiv.org/abs/1904.07272 [Chapter 1]

==Homeworks ==

== Projects ==