Moses - Advanced topics in Reinforcement Learning

Display language

Diese Modulbeschreibung gilt nicht im aktuellen Semester.

Die Modulbeschreibung gilt nur für den Zeitraum WS 2019/20 - SoSe 2022. Die Version für das aktuelle Semester (SoSe 2024) finden Sie hier: Modulbeschreibung des SoSe 2024

Module / Version:
#40969 / #1

Validity:
WS 2019/20 - SoSe 2022

Default display language:
English

Module title:
Advanced topics in Reinforcement Learning

Credits:
6

Responsible person:
Obermayer, Klaus

Grading:
benotet

Type of exam:
Mündliche Prüfung

Zugehörigkeit

Faculty:
Fakultät IV

Institute:
Institut für Softwaretechnik und Theoretische Informatik

Area of expertise:
34351300 FG Neuronale Informationsverarbeitung

Prüfungsausschuss:
No information

Kontakt

Office:
MAR 5-6

Contact person:
Groiß, Camilla

E-mail address:
sekr@ni.tu-berlin.de

Website:

http://www.ni.tu-berlin.de/teaching/

Learning Outcomes

After successful completion, participants are able to apply at least one state-of-the-art technique in Reinforcement Learning (more specifically in topics related to meta-learning) and present their results to other students. They will be able to reproduce and present scientific research and also have a more specific and deeper knowledge of how the brain works depending on reinforcement.

Content

Participants will search the recent literature on Reinforcement Learning, present one state-of-the-art method of their choice and then apply it on a toy example.

Module Components

Pflichtgruppe:

All Courses are mandatory.

Course Name	Type	Number	Cycle	Language	SWS	VZ
Advanced topics in Reinforcement Learning	SEM	3435 L 10262	WiSe/SoSe	English	2

Workload and Credit Points

Advanced topics in Reinforcement Learning (SEM):

Workload description	Multiplier	Hours	Total
Attendance	15.0	2.0h	30.0h
Pre/post processing	15.0	10.0h	150.0h
			180.0h(~6 LP)

The Workload of the module sums up to 180.0 Hours. Therefore the module contains 6 Credits.

Description of Teaching and Learning Methods

Participants will research the current relevant literature and prepare a presentation of a new method in Reinforcement Learning to the other students. In the sequel they will apply their method to one of the toy examples and present the results. Language of the module is English.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Required skills include: literature research, presentation of researched articles, basic understanding of metacognition Participants are expected to have background knowledge comparable to the topics covered in Machine Intelligence I + II .

Mandatory requirements for the module test application:

This module has no requirements.

Module completion

Grading

graded

Type of exam

Oral exam

Language

English

Duration/Extent

45 minutes

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:
Winter- und Sommersemester.

Maximum Number of Participants

The maximum capacity of students is 30.

Registration Procedures

registration via yellow sheet from the examination board; exam takes place in English language sekr@ni.tu-berlin.de

Recommended literature
No recommended literature given

Assigned Degree Programs

Semester:

Zeige auch ausgelaufene Studiengänge und StuPOs

This module is used in the following Degree Programs (new System):

	Studiengang / StuPO	StuPOs	Verwendungen	Erste Verwendung	Letzte Verwendung
This module is not used in any degree program.

Students of other degrees can participate in this module without capacity testing.

No information

Miscellaneous