Reinforcement learning 2021 2022 — различия между версиями
Материал из Wiki - Факультет компьютерных наук
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==Homeworks == | ==Homeworks == | ||
| − | *[https://www.dropbox.com/s/k2at9lixvshpcbw/HW_1_RL_2021.pdf?dl=0 '''Homework №1, deadline 14.12.2021, 23:59'''], | + | *[https://www.dropbox.com/s/k2at9lixvshpcbw/HW_1_RL_2021.pdf?dl=0 '''Homework №1, deadline 14.12.2021, 23:59'''], [https://www.dropbox.com/s/l7pma6kwnopl856/HW_1_task_2.ipynb?dl=0 '''Environment for task №2'''], |
== Projects == | == Projects == | ||
Версия 14:58, 5 декабря 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
