Department for Informatics | Sitemap | LMU-Portal
Deutsch
  • Home
  • Future Students
  • Enrolled students
  • Teaching
    • Archive
    • SS 2023
      • CC
      • CG1
      • DS
      • DW2
      • EVM
      • HCA
      • HS
      • MMI1
      • MMP
      • MT
      • PAR
      • PEM_RL
      • PGD
      • PKMM
      • PML
      • PS
      • PSK
      • PSYA1
      • PSYG2
      • PTP
      • PVRU
      • SWH
      • SWH-NEBENFACH
      • USEC
      • UX2
      • UX3
      • WAL
  • Research
  • People
  • Contact
  • Jobs
  • Internal
  • COVID-19 special: online teaching
PEM_RL in other semesters:
SS23 WS2122 WS2021
Home > Teaching > SS 2023 > PEM_RL

Practical Course Development of Media Systems: Reinforcement Learning

Lecturers: Yannick Weiss, Jesse Grootjen, Florian Bemmann Professor in Charge: Prof. Sven Mayer
Hours per week: 4
ECTS-Credits: 6
Modul: Master P5.0.2 oder P5.0.4: Gruppenpraktikum zu fortgeschrittenen Themen der Informatik I oder Informatik II
After consultation with the examination board, credit for P2, P3 or P6 (advanced topics for Master) also possible

  • News
  • Contents
    • Inspiring Papers
  • Other lectures on the topic
  • Schedule
  • Location
  • Application


News

  • 09.05.2023: Please sign up for our Moodle course SS23 PEM RL. The password will be provided in the first session.
  • 08.02.2023: This page is still under development, all content may be subject to change.


Contents

We work on solutions for the use of Machine Learning for Media Systems. During the first half we will learn some basics and consolidate it in small exercises using Python and Unity. In the second hald, you should implement your own ideas and present it as a prototype. The ideas will be implemented and presented as a prototype.

Inspiring Papers

Authors Venue Title
Yonghao Long, Wang Wei, Tao Huang, Yuehao Wang and Qi Dou Submitted to ICRA 2023 Human-in-the-loop Embodied Intelligence with Interactive Simulation Environment for Surgical Robot Learning
Gupta, T., & Gori, J. CHI EA 2023 Modeling reciprocal adaptation in HCI: a Multi-Agent Reinforcement Learning Approach
Ahadi-Sarkani, Armand and Elmalaki, Salma CPHS 2021 ADAS-RL: Adaptive Vector Scaling Reinforcement Learning For Human-in-the-Loop Lane Departure Warning
Óscar Pérez‑Gil et al. Multimedia Tools and Applications 2022 Deep reinforcement learning based control for Autonomous Vehicles in CARLA
Kashyap Todi, Gilles Bailly, Luis Leiva, and Antti Oulasvirta. 2021. CHI 2021 Adapting User Interfaces with Model-based Reinforcement Learning
Saurav Singh, Jamison Heard HRI 2022 Human-Aware Reinforcement Learning for Adaptive Human Robot Teaming
Jonas Tjomsland, Ali Shafti, A. Aldo Faisal NeurIPS 2019 Human-Robot Collaboration via Deep Reinforcement Learning of Real-World Interactions
Chengxi Li et al. AAMAS 2018 An Ar-Assisted Deep Reinforcement Learning-Based Approach Towards Mutual-Cognitive Safe Human-Robot Interaction
Hannes Ritschel AAMAS 2018 Socially-Aware Reinforcement Learning for Personalized Human-Robot Interaction
Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf WWW 2023 Psychotherapy AI Companion with Reinforcement Learning Recommendations and Interpretable Policy Dynamics
Mojtaba Taherisadr, Stelios Andrew Stavroulakis, Salma Elmalaki IoTDI 2023 adaPARL: Adaptive Privacy-Aware Reinforcement Learning for Sequential Decision Making Human-in-the-Loop Systems


Other lectures on the topic

  • Human Computer Interaction
  • Machine Learning, e.g. Pratical Machine Learning, Intelligent User Interfaces


Schedule

Please note that due to the engaging nature of the group projects, students have to spend more than the allocated time on the development of their prototypes. This is especially true for the group project implementation scheduled during the lecture break.

Date Time Topic
09.05.2023 16:00-18:00 Lecture 1: Introduction & Project Brainstorming
23.05.2023 16:00-18:00 Lecture 2: 90-sec Paper Presentation
06.06.2023 16:00-18:00 Lecture 3: Project Ideation & Group Formation
20.06.2023 16:00-18:00 Lecture 4
04.07.2023 16:00-18:00 Lecture 5
18.07.2023 16:00-18:00 Lecture 6
28.08.2023 09:00-17:00 Work on the projects
29.08.2023 09:00-17:00 Work on the projects
30.08.2023 09:00-17:00 Work on the projects
31.08.2023 09:00-17:00 Work on the projects
01.09.2023 09:00-12:00 Work on the projects
01.09.2023 12:00-17:00 Final presentations


Location

The course takes at Location: Frauenlobstr. 7a, Room 357



Application

Interested students can apply for this practical course via Uni2Work .

The applications should include the following information:

  • Describe relevant expertise, for example from previous courses, jobs and other projects, if any, that demonstrate your skills.
  • If you already have a project idea that you would like to implement in this course, please sketch it out briefly.
To top
Impressum – Privacy policy – Contact  |  Last modified on 20230606 10:15 AM