FS2025: 42111/62111 Reinforcement Learning and Decision Making Under Uncertainty

The course will give a thorough introduction to the theory and algorithms of reinforcement learning and decision theory more generally. Can be taken in parallel with the advanced seminar course. It is mainly addressed to students in computer science, mathematics, statistics and economics.

Allgemeine Informationen

Kursbeschreibung

Good knowledge of probability, linear algebra and calculus is necessary.

This course covers the fundamental theory and algorithms in reinforcement learning. For a more detailed look into current research, you are advised to take the course "Seminar Advanced Topics in Learning and Decision Making" in parallel.

Kursprogramm

- Decision theory
- Bayesian analysis
- Markov decision processes
- Stochastic approximation algorithms for RL
- Gradient algorithms for RL
- Function approximation for RL
- Regret bounds in RL
- Inverse reinforcement learning
- Multi-agent games and RL

Zielgruppe

Motivated Master students in computer science, mathematics, statistics or economics, that wish to obtain background knowledge in the field and later pursue a MSc thesis in the area.

Beschreibung

The course will give a thorough introduction to the theory and algorithms of reinforcement learning and decision theory more generally. Can be taken in parallel with the advanced seminar course.

It is mainly addressed to students in computer science, mathematics, statistics and economics.

Allgemein

Sprache

Deutsch

Verfügbarkeit

Zugriff

Unbegrenzt – wenn online geschaltet

Aufnahmeverfahren

Sie können diesem Kurs direkt beitreten.

Zeitraum für Beitritte

Unbegrenzt

Für Kursadministratoren freigegebene Daten

Daten des Persönlichen Profils

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