Reinforcement Learning with Application to autonomous Systems


Learning Objectives

1.1 Subject-specific Competencies
gain insight into the theory and applications of reinforcement learning
learn to analyze the challenges in a reinforcement learning application and to identify promising
learning approaches.
are able to assess for which problems reinforcement learning is particularly well suited and which
disadvantages exist with regard to this.
understand, explain, and classify relevant basic concepts.

1.2 Methodological Competencies
can evaluate the properties of different learning strategies depending on the problem.
are able to implement selected methods with the programming language. Python and with the help
of suitable frameworks.

1.3 Interdisciplinary Competencies
can evaluate solutions in highly complex environments.
are able to acquire detailed knowledge for solving specific problems on their own.
learn to discuss and derive solutions within a team.

Course Content

  Introduction to Decision Making (Reward Hypothesis, Markov Reward and Decision Processes,
    Value and Policy Iteration, Bellman Equation).
Basic principles (Exploration and Exploitation, On and Off-policy learning, model-free and model-
    based policy learning.
Algorithmic principles: Q-learning, SARSA, TD-learning, function approximation.
Introduction to deep reinforcement learning as well as basic concepts of deep learning.


R. S. Sutton, A. G. Barto; Reinforcement Learning - An Introduction; MIT Press; 2nd Edition;
C. Szepesvari; Algorithms for Reinforcement Learning; Morgan & Claypool Publishers; 2010

Sprache Englisch
Dozent Christopher Knievel
Fakultät EI
Technisch / Wirtschaftlich Technisch
Studiengänge Elektrische Systeme (EIM)
International Project Engineering (IPE)
Plätze -
Semester WS 2024/25