Reinforcement learning 2021 2022 — различия между версиями
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== Recommended literature == | == Recommended literature == | ||
'''Lecture and seminar 09.11''' | '''Lecture and seminar 09.11''' | ||
| − | * Sebastien Bubek, Nicolo Cesa-Bianchi. Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems. Chapter 2. | + | * 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 | + | * Richard S. Sutton, Andrew G. Barto. Reinforcement Learning: An Introduction. Chapter 2. http://incompleteideas.net/book/the-book-2nd.html; |
| − | + | ||
==Homeworks == | ==Homeworks == | ||
== Projects == | == Projects == | ||
Версия 23:20, 9 ноября 2021
Содержание
Lecturers and Seminarists
| Lecturer | Naumov Alexey | [anaumov@hse.ru] | T924 |
| Lecturer | Denis Belomestny | [dbelomestny@hse.ru] | T924 |
| Seminarist | Samsonov Sergey | [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;
