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Reinforcement learning 2021 2022 - История изменений
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<p><b>Новая страница</b></p><div>== Lecturers and Seminarists ==<br />
<br />
{| class="wikitable" style="text-align:center"<br />
|-<br />
|| Lecturer || [https://www.hse.ru/staff/anaumov Alexey Naumov] || [anaumov@hse.ru] || T924<br />
|- <br />
|| Lecturer || [https://www.hse.ru/org/persons/93130881 Denis Belomestny ] || [dbelomestny@hse.ru] || T924<br />
|- <br />
|| Seminarist || [https://www.hse.ru/org/persons/219484540 Sergey Samsonov] || [svsamsonov@hse.ru] || T926<br />
|-<br />
|| Seminarist || [https://www.hse.ru/staff/mkaledin Maxim Kaledin ] || [mkaledin@hse.ru] || T926<br />
|-<br />
|}<br />
<br />
== About the course ==<br />
This page contains materials for Mathematical Foundations of Reinforcement learning course in 2021/2022 year, optional one for 2nd year Master students of the Math of Machine Learning program (HSE and Skoltech).<br />
<br />
== 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> = 0.5*O<sub>HW</sub> + 0.5*O<sub>Project</sub><br />
with the usual (arithmetical) rounding rule.<br />
<br />
[https://docs.google.com/spreadsheets/d/1MPWVIkgxyotHU-P5cE7Gik4C6RTWxTnAVK8Btl7Fw3Y/edit?usp=sharing '''Table with grades''']<br />
<br />
== Lectures ==<br />
*[https://www.dropbox.com/s/a69ql9duo5jf5gt/Math%20of%20RL%20Lecture%201.pdf?dl=0 ''' Lecture 09.11''']<br />
*[https://www.dropbox.com/s/7zkirk1xykua890/Math_of_RL_Le%20cture_2.pdf?dl=0 ''' Lecture 16.11''']<br />
<br />
== Seminars ==<br />
*[https://www.dropbox.com/s/wc951vseud1q1p2/Seminar_09_11_RL.pdf?dl=0 '''Seminar 09.11'''], [https://www.dropbox.com/s/2h83vbjgew1inen/Seminar_1_RL.mp4?dl=0 '''Seminar 09.11, Video'''], [https://www.dropbox.com/s/bxa8h9vjrnegsql/Bandit_intro_strategies_09_11_2021.ipynb?dl=0 '''Seminar 09.11, Notebook''']<br />
*[https://www.dropbox.com/s/cq0t2o6n4yn6oag/Seminar_16_11_RL.mp4?dl=0 '''Seminar 16.11, Video'''],<br />
*[https://www.dropbox.com/s/ex8v9w3smar70m7/Seminar_23_11_RL.mp4?dl=0 '''Seminar 23.11, Video'''],<br />
*[https://www.dropbox.com/s/v1ywnk8eyhourjq/Seminar_07_12_RL.mp4?dl=0 '''Seminar 07.12, Video'''],<br />
<br />
== Recommended literature ==<br />
<br />
'''Lecture and seminar 09.11'''<br />
<br />
* 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 />
<br />
'''Lecture and seminar 16.11'''<br />
*[https://www.dropbox.com/s/wc951vseud1q1p2/Seminar_09_11_RL.pdf?dl=0 '''Seminar 09.11'''], [https://www.dropbox.com/s/2h83vbjgew1inen/Seminar_1_RL.mp4?dl=0 '''Seminar 09.11, Video'''], <br />
<br />
==Homeworks ==<br />
*[https://www.dropbox.com/s/k2at9lixvshpcbw/HW_1_RL_2021.pdf?dl=0 '''Homework №1, deadline 19.12.2021, 23:59'''], [https://www.dropbox.com/s/l7pma6kwnopl856/HW_1_task_2.ipynb?dl=0 '''Environment for task №2'''],<br />
*[https://www.dropbox.com/s/jynwji3dw3xxjww/HW_2_RL_2021.pdf?dl=0 '''Homework №2, deadline 19.12.2021, 23:59'''].<br />
<br />
== Projects ==</div>
imported>Svsamsonov