Reinforcement learning 2022 2023 — различия между версиями

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* 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;
 
* Botao Hao et al. Bootstrapping Upper Confidence Bound. https://arxiv.org/abs/1906.05247
 
* Botao Hao et al. Bootstrapping Upper Confidence Bound. https://arxiv.org/abs/1906.05247
* Aleksandrs Slivkins. Introduction to Multi-Armed Bandits. https://arxiv.org/abs/1904.07272
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* Aleksandrs Slivkins. Introduction to Multi-Armed Bandits. https://arxiv.org/abs/1904.07272 [Chapter 1]
  
 
==Homeworks ==
 
==Homeworks ==

Текущая версия на 14:39, 21 ноября 2022

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.

Table with grades

Course materials

Recommended literature

Homeworks

Projects