Moses - Advanced topics in Reinforcement Learning II: Multi-Agent Systems and Hierarchical Learning

Display language

To modulepage Generate PDF

Diese Modulbeschreibung gilt nicht im aktuellen Semester.

Die Modulbeschreibung gilt nur für den Zeitraum SoSe 2020 - SoSe 2022.

Module / Version:
#41002 / #1

Validity:
SoSe 2020 - SoSe 2022

Default display language:
English

Module title:
Advanced topics in Reinforcement Learning II: Multi-Agent Systems and Hierarchical Learning

Credits:
6

Responsible person:
Obermayer, Klaus

Grading:
benotet

Type of exam:
Portfolioprüfung

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:
Laschos, Vaios

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

Website:
No information

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 hierarchical learning or/and multi agent system) and present their results to other students. They will be able to reproduce and present scientific research.

Content

Participants will search the recent literature on Reinforcement Learning (more specifically in topics related to hierarchical learning or/and multi agent system), 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 II: Multi-Agent Systems and Hierarchical Learning	SEM	3435 L 10633	SoSe	English	4

Workload and Credit Points

Advanced topics in Reinforcement Learning II: Multi-Agent Systems and Hierarchical Learning (SEM):

Workload description	Multiplier	Hours	Total
Attendance	15.0	4.0h	60.0h
Pre/post processing	15.0	8.0h	120.0h
			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

Portfolio examination

Type of portfolio examination

100 Punkte insgesamt

Language

English

Test elements

Name	Points	Categorie	Duration/Extent
(Deliverable assessment) Code	30	practical	30 hours
(Deliverable assessment) Presentation	70	practical	40 minutes

Grading scale

Notenschlüssel »Notenschlüssel 2: Fak IV (2)«

Gesamtpunktzahl	1.0	1.3	1.7	2.0	2.3	2.7	3.0	3.3	3.7	4.0
100.0pt	95.0pt	90.0pt	85.0pt	80.0pt	75.0pt	70.0pt	65.0pt	60.0pt	55.0pt	50.0pt

Test description (Module completion)

No information

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:
Sommersemester.

Maximum Number of Participants

The maximum capacity of students is 30.

Registration Procedures

Way of registration is not established yet. It is going to be either via yellow sheet from the examination board or electronic; 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.

Miscellaneous