Reinforcement learning 2022 2023 — различия между версиями
Материал из Wiki - Факультет компьютерных наук
| Строка 17: | Строка 17: | ||
* O<sub>Project</sub> for the course project | * O<sub>Project</sub> for the course project | ||
The formula for the final grade is | The formula for the final grade is | ||
| − | * O<sub>Final</sub> = 0. | + | * O<sub>Final</sub> = 0.6*O<sub>HW</sub> + 0.4*O<sub>Project</sub> |
with the usual (arithmetical) rounding rule. | with the usual (arithmetical) rounding rule. | ||
Версия 19:04, 11 ноября 2022
Содержание
Lecturers and Seminarists
| Lecturer | Alexey Naumov | [anaumov@hse.ru] | T924 |
| Seminarist | Sergey Samsonov | [svsamsonov@hse.ru] | T926 |
About the course
This page contains materials for Mathematical Foundations of Reinforcement learning course in 2022/2023 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 = 0.6*OHW + 0.4*OProject
with the usual (arithmetical) rounding rule.
Lectures
Seminars
- Seminar 09.11, Seminar 09.11, Video, Seminar 09.11, Notebook
- Seminar 16.11, Video,
- Seminar 23.11, Video,
- Seminar 07.12, Video,
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
Homeworks
- Homework №1, deadline 19.12.2021, 23:59, Environment for task №2,
- Homework №2, deadline 19.12.2021, 23:59.
