Reinforcement learning 2021 2022: различия между версиями
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*[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'''] | *[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'''] | ||
== Recommended literature == | == Recommended literature == | ||
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* Sebastien Bubek, Nicolo Cesa-Bianchi. Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems. Chapter 2. \url{http://sbubeck.com/SurveyBCB12.pdf} | * Sebastien Bubek, Nicolo Cesa-Bianchi. Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems. Chapter 2. \url{http://sbubeck.com/SurveyBCB12.pdf} | ||
* Richard S. Sutton, Andrew G. Barto. Reinforcement Learning: An Introduction. Chapter~$2$. \url{http://incompleteideas.net/book/the-book-2nd.html}; | * Richard S. Sutton, Andrew G. Barto. Reinforcement Learning: An Introduction. Chapter~$2$. \url{http://incompleteideas.net/book/the-book-2nd.html}; | ||
==Homeworks == | |||
== Projects == | |||
Версия от 20:20, 9 ноября 2021
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. \url{http://sbubeck.com/SurveyBCB12.pdf}
- Richard S. Sutton, Andrew G. Barto. Reinforcement Learning: An Introduction. Chapter~$2$. \url{http://incompleteideas.net/book/the-book-2nd.html};