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Introduction to Deep Reinforcement Learning

MGT001299Master's Modules6 ECTSEnglishwinter semesterProfessur für Business Analytics and Intelligent Systems (Prof. Minner komm.)
AI-edited module sheet. Based on the TUMonline module description, edited for readability.Original in TUMonline

What it is about

You learn the theory and fundamentals of Deep Reinforcement Learning (DRL) as well as relevant deep-learning and reinforcement-learning concepts. By the end you will be able to model problems as Markov Decision Processes, understand and apply DRL algorithms, and assess their advantages and disadvantages.

What you will be able to do

  • Foundations of search algorithms (graph and tree search)
  • Model problems as Markov Decision Process (MDP)
  • Foundations of Reinforcement Learning (e.g., Q‑Learning, TD‑Learning)
  • Foundations of Deep Learning (e.g., SGD, logistic regression, neural networks)
  • Understand Deep Reinforcement Learning (e.g., DQN, PPO)
  • Apply DRL frameworks to practical problems
  • Evaluate advantages and disadvantages of DRL methods
  • Recognize and handle common pitfalls in practical applications

What the module consists of

  • VorlesungVermittlung der theoretischen Grundlagen von Deep Reinforcement Learning
  • ÜbungenAnwendung und Vertiefung des Vorlesungsinhalts
  • Coding LabsPraktische Umsetzung und Implementierung von DRL‑Methoden

Teaching method

  • VorlesungErklärung der theoretischen Konzepte
  • ÜbungenPraktische Vertiefung und methodische Anwendung
  • Coding LabsProgrammierung und Anwendung von DRL auf praxisnahe Probleme

Dates

LectureIntroduction to Deep Reinforcement Learning (MGT001299, englisch)3 groups to choose from

  • AThu14:00–17:00Online: Videokonferenz
    13× · 22.10.–04.02.
    • 22.10.
    • 29.10.
    • 05.11.
    • 12.11.
    • 19.11.
    • 26.11.
    • 10.12.
    • 17.12.
    • 07.01.
    • 14.01.
    • 21.01.
    • 28.01.
    • 04.02.
  • BFri13:15–14:45Theresianum, 0602, Hörsaal ansteigend, ohne exp. B (0506.EG.602)
    14× · 16.10.–05.02.
    • 16.10.
    • 30.10.
    • 06.11.
    • 13.11.
    • 20.11.
    • 27.11.
    • 04.12.
    • 11.12.
    • 18.12.
    • 08.01.
    • 15.01.
    • 22.01.
    • 29.01.
    • 05.02.
  • CFri16:30–19:30Online: Videokonferenzonce on 05.02.

ExerciseIntroduction to Deep Reinforcement Learning (MGT001299, englisch) (Exercise)

  • Fri15:00–16:30Theresianum, 0602, Hörsaal ansteigend, ohne exp. B (0506.EG.602)
    14× · 16.10.–05.02.
    • 16.10.
    • 30.10.
    • 06.11.
    • 13.11.
    • 20.11.
    • 27.11.
    • 04.12.
    • 11.12.
    • 18.12.
    • 08.01.
    • 15.01.
    • 22.01.
    • 29.01.
    • 05.02.

From the current semester, not binding. You attend one of several groups; the timetable automatically suggests the one with the fewest clashes.

Show TUMonline data
Sprache
Englisch
Turnus
Wintersemester
Modulniveau
Master
Moduldauer
Einsemestrig
Gesamtstunden
180
Präsenzstunden
60
Eigenstudiumstunden
120
Organisationsname
Professur für Business Analytics and Intelligent Systems (Prof. Minner komm.)

Courses

  • Introduction to Deep Reinforcement Learning
  • Introduction to Deep Reinforcement Learning (Exercise)
  • Introduction to Deep Reinforcement Learning (Lecture)

Official page in TUMonline · Details are not binding.