Reinforcement learning 2021 2022 — различия между версиями

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== Recommended literature ==
 
== Recommended literature ==
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'''Lecture and seminar 09.11'''
 
'''Lecture and seminar 09.11'''
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* Sebastien Bubek, Nicolo Cesa-Bianchi. Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems. Chapter 2. http://sbubeck.com/SurveyBCB12.pdf
 
* 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;
 
* Richard S. Sutton, Andrew G. Barto. Reinforcement Learning: An Introduction. Chapter 2. http://incompleteideas.net/book/the-book-2nd.html;
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* Botao Hao et al. Bootstrapping Upper Confidence Bound. https://arxiv.org/abs/1906.05247
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'''Lecture and seminar 16.11'''
  
 
==Homeworks ==
 
==Homeworks ==
  
 
== Projects ==
 
== Projects ==

Версия 23:21, 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


Recommended literature

Lecture and seminar 09.11

Lecture and seminar 16.11

Homeworks

Projects