Магистратура
2022/2023
Математика обучения с подкреплением
Статус:
Курс по выбору (Математика машинного обучения)
Направление:
01.04.02. Прикладная математика и информатика
Кто читает:
Кафедра технологий моделирования сложных систем
Где читается:
Факультет компьютерных наук
Когда читается:
2-й курс, 2 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для своего кампуса
Преподаватели:
Беломестный Денис Витальевич,
Каледин Максим Львович,
Наумов Алексей Александрович,
Самсонов Сергей Владимирович
Прогр. обучения:
Математика машинного обучения
Язык:
английский
Кредиты:
6
Контактные часы:
32
Course Syllabus
Abstract
Reinforcement Learning is a fascinating area located on the intersection of approximation techniques, optimal control, statistics and machine learning. The main problem sounds as follows: ”For some agent in some (possibly adaptive) environment, how to learn a way to make decisions to live ”optimally” by know- ing only some scalar reward obtained after taking the action?” It can be argued that this is essentially an optimal control problem... Yes and no. Yes – because the goal is to learn control function for the agent, which would tell what to do in certain state of the world. No – because classic optimal control usually deals with known model of the environment (transi- tion probabilities, stochastic differential equations,..). Reinforcement Learning is concerned with what to do if such model is unavailable and only some general assumptions can be made about its function. There are several decent courses on Reinforcement Learning existing, most of them are practical: in the sense that they introduce many algorithms and ideas of solutions for certain practical problems. The other side of the story, mathematical explanations of why the methods actually work, is mostly skipped. Our course is aimed at closing this gap and focusing mainly on the mathematics behind Reinforcement Learning.".