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263-5255-00L 7 Credits DR , MSC , WBZ D-ITET , D-INFK , D-MATH
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Theoretical Foundations of Reinforcement Learning

Foundations of Reinforcement Learning

Lecturers & Examiners: Prof. Dr. Niao He
VVZ CR 2.29

Last Updated: 2026-06-01 11:33:07

Abstract

Reinforcement learning (RL) has been in the limelight of many recent breakthroughs in artificial intelligence. This course focuses on theoretical and algorithmic foundations of reinforcement learning, through the lens of optimization, modern approximation, and learning theory. The course targets M.S. students with strong research interests in reinforcement learning, optimization, and control.

Objective

This course aims to provide students with an advanced introduction of RL theory and algorithms as well as bring them near the frontier of this active research field. By the end of the course, students will be able to - Identify the strengths and limitations of various reinforcement learning algorithms; - Formulate and solve sequential decision-making problems by applying relevant reinforcement learning tools; - Generalize or discover new algorithms, or theories of reinforcement learning towards conducting independent research on the topic.

Content

Topics include fundamentals of Markov decision processes, approximate dynamic programming, linear programming and primal-dual perspectives of RL, model-based and model-free RL, policy gradient and actor-critic algorithms, Markov games and multi-agent RL. If time allows, we will also discuss advanced topics such as batch RL, inverse RL, causal RL, etc. The course keeps strong emphasis on in-depth understanding of the mathematical modeling and theoretical properties of RL algorithms.

Resources

Lecture Notes

Lecture slides will be posted on Moodle.

Literature

Dynamic Programming and Optimal Control, Vol I & II, Dimitris Bertsekas Reinforcement Learning: An Introduction, Second Edition, Richard Sutton and Andrew Barto. Algorithms for Reinforcement Learning, Csaba Czepesvári. Reinforcement Learning: Theory and Algorithms, Alekh Agarwal, Nan Jiang, Sham M. Kakade.

Learning Materials (Links)

General Information

Language
English
Levels
DR , MSC , WBZ
Frequency
Yearly recurring

Examination

Type
graded semester performance
Midterm Exam 40%, Assignments 35%, Paper Presentation 25%!Last cancellation/deregistration date for this graded semester performance: February 28, 2025! Please note that after that date no deregistration will be accepted and the course will be considered as "fail".

Registration & Places

Max Places
50
Priority: Registration for the course unit is until 28.02.2025 only possible for the primary target group

Course Components

Type Title Time & Place Hours
lecture Foundations of Reinforcement Learning
  • Mon 09:15-12:00 (CHN C 14)
3 h weekly
independent project Foundations of Reinforcement Learning No time listed 3 h weekly

Offered In