Reinforcement learning 2021 2022: различия между версиями

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== Seminars ==
== Seminars ==
*[https://www.dropbox.com/s/i5g7a1pnbsnwclm/Seminar_18_09.pdf?dl=0 '''Seminar 09.11'''], [https://www.dropbox.com/s/i5g7a1pnbsnwclm/Seminar_18_09.pdf?dl=0 '''Seminar 09.11, Video''']
*[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''']


==Homeworks ==
==Homeworks ==

Версия от 20:12, 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.

Table with grades

Lectures

Seminars

Homeworks

Projects

Recommended literature (1st term)