https://www.wikicshse.ru/index.php?action=history&feed=atom&title=Reinforcement_learning_2022_2023Reinforcement learning 2022 2023 - История изменений2026-06-06T12:14:14ZИстория изменений этой страницы в викиMediaWiki 1.45.3https://www.wikicshse.ru/index.php?title=Reinforcement_learning_2022_2023&diff=635&oldid=previmported>Svsamsonov: Migrated current public revision from wiki.cs.hse.ru2022-11-21T11:39:15Z<p>Migrated current public revision from wiki.cs.hse.ru</p>
<p><b>Новая страница</b></p><div>== Lecturers and Seminarists ==<br />
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{| class="wikitable" style="text-align:center"<br />
|-<br />
|| Lecturer || [https://www.hse.ru/staff/anaumov Alexey Naumov] || [anaumov@hse.ru] || T924<br />
|- <br />
|| Seminarist || [https://www.hse.ru/org/persons/219484540 Sergey Samsonov] || [svsamsonov@hse.ru] || T926<br />
|-<br />
|}<br />
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== About the course ==<br />
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).<br />
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== Grading == <br />
The final grade consists of 2 components (each is non-negative real number from 0 to 10, without any intermediate rounding) :<br />
* O<sub>HW</sub> for the hometasks<br />
* O<sub>Project</sub> for the course project<br />
The formula for the final grade is <br />
* O<sub>Final</sub> = 0.6*O<sub>HW</sub> + 0.4*O<sub>Project</sub><br />
with the usual (arithmetical) rounding rule.<br />
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[https://docs.google.com/spreadsheets/d/1MPWVIkgxyotHU-P5cE7Gik4C6RTWxTnAVK8Btl7Fw3Y/edit?usp=sharing '''Table with grades''']<br />
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== Course materials ==<br />
*[https://www.overleaf.com/read/kbzmvxdzbrxq '''Lectures and seminars notes''']<br />
*[https://colab.research.google.com/drive/10qBq7Ot_1ZpnTeD11P5AnE8jFVj0OLXl?usp=sharing '''Notebook for the first seminar''']<br />
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== Recommended literature ==<br />
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* Sebastien Bubek, Nicolo Cesa-Bianchi. Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems. Chapter 2. http://sbubeck.com/SurveyBCB12.pdf<br />
* Richard S. Sutton, Andrew G. Barto. Reinforcement Learning: An Introduction. Chapter 2. http://incompleteideas.net/book/the-book-2nd.html;<br />
* Botao Hao et al. Bootstrapping Upper Confidence Bound. https://arxiv.org/abs/1906.05247<br />
* Aleksandrs Slivkins. Introduction to Multi-Armed Bandits. https://arxiv.org/abs/1904.07272 [Chapter 1]<br />
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==Homeworks ==<br />
*[https://github.com/svsamsonov/Math_RL_2022_2023 '''HW #1, deadline: 04.12.22, 23:59''']<br />
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== Projects ==</div>imported>Svsamsonov