Reinforcement learning 2021 2022 — различия между версиями
Материал из Wiki - Факультет компьютерных наук
| Строка 27: | Строка 27: | ||
== Lectures == | == Lectures == | ||
| + | *[https://www.dropbox.com/s/a69ql9duo5jf5gt/Math%20of%20RL%20Lecture%201.pdf?dl=0 ''' Lecture 09.11'''] | ||
| + | *[https://www.dropbox.com/s/7zkirk1xykua890/Math_of_RL_Le%20cture_2.pdf?dl=0 ''' Lecture 16.11'''] | ||
== Seminars == | == Seminars == | ||
Версия 00:15, 21 ноября 2021
Содержание
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
