The spring semester 2021 will certainly take place online until Easter. Exceptions: Courses that can only be carried out with on-site presence. Please note the information provided by the lecturers.

227-0559-00L  Seminar in Deep Reinforcement Learning

SemesterSpring Semester 2019
LecturersR. Wattenhofer, O. Richter
Periodicityyearly recurring course
Language of instructionEnglish
CommentNumber of participants limited to 24.



Catalogue data

AbstractIn this seminar participating students present and discuss recent research papers in the area of deep reinforcement learning. The seminar starts with two introductory lessons introducing the basic concepts. Alongside the seminar a programming challenge is posed in which students can take part to improve their grade.
ObjectiveSince Google Deepmind presented the Deep Q-Network (DQN) algorithm in 2015 that could play Atari-2600 games at a superhuman level, the field of deep reinforcement learning gained a lot of traction. It sparked media attention with AlphaGo and AlphaZero and is one of the most prominent research areas. Yet many research papers in the area come from one of two sources: Google Deepmind or OpenAI. In this seminar we aim at giving the students an in depth view on the current advances in the area by discussing recent papers as well as discussing current issues and difficulties surrounding deep reinforcement learning.
ContentTwo introductory courses introducing Q-learning and policy gradient methods. Afterwards participating students present recent papers. For details see: www.disco.ethz.ch/courses.html
Lecture notesSlides of presentations will be made available.
LiteratureOpenAI course (https://spinningup.openai.com/en/latest/) plus selected papers.
The paper selection can be found on www.disco.ethz.ch/courses.html.
Prerequisites / NoticeIt is expected that student have prior knowledge and interest in machine and deep learning, for instance by having attended appropriate courses.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits2 credits
ExaminersR. Wattenhofer, O. Richter
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.

Learning materials

 
Main linkInformation
Only public learning materials are listed.

Courses

NumberTitleHoursLecturers
227-0559-00 SSeminar in Deep Reinforcement Learning2 hrs
Tue10-12ETZ G 91 »
R. Wattenhofer, O. Richter

Groups

No information on groups available.

Restrictions

PlacesLimited number of places. Special selection procedure.
Waiting listuntil 18.02.2019
End of registration periodRegistration only possible until 12.02.2019

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