Reinforcement learning 2021 2022: различия между версиями
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|| Lecturer || [https://www.hse.ru/staff/anaumov Naumov | || Lecturer || [https://www.hse.ru/staff/anaumov Alexey Naumov] || [anaumov@hse.ru] || T924 | ||
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|| Lecturer || [https://www.hse.ru/org/persons/93130881 Denis Belomestny ] || [dbelomestny@hse.ru] || T924 | || Lecturer || [https://www.hse.ru/org/persons/93130881 Denis Belomestny ] || [dbelomestny@hse.ru] || T924 | ||
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|| Seminarist || [https://www.hse.ru/org/persons/219484540 Samsonov | || Seminarist || [https://www.hse.ru/org/persons/219484540 Sergey Samsonov] || [svsamsonov@hse.ru] || T926 | ||
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|| Seminarist || [https://www.hse.ru/staff/mkaledin Maxim Kaledin ] || [mkaledin@hse.ru] || T926 | || Seminarist || [https://www.hse.ru/staff/mkaledin Maxim Kaledin ] || [mkaledin@hse.ru] || T926 | ||
Версия от 20:22, 9 ноября 2021
Lecturers and Seminarists
| Lecturer | Alexey Naumov | [anaumov@hse.ru] | T924 |
| Lecturer | Denis Belomestny | [dbelomestny@hse.ru] | T924 |
| Seminarist | Sergey Samsonov | [svsamsonov@hse.ru] | T926 |
| Seminarist | Maxim Kaledin | [mkaledin@hse.ru] | T926 |
About the course
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).
Grading
The final grade consists of 2 components (each is non-negative real number from 0 to 10, without any intermediate rounding) :
- OHW for the hometasks
- OProject for the course project
The formula for the final grade is
- OFinal = 0.5*OHW + 0.5*OProject
with the usual (arithmetical) rounding rule.
Lectures
Seminars
Recommended literature
Lecture and seminar 09.11
- 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
Lecture and seminar 16.11