Special Topics: Reinforcement Learning
344.056, KV, 3h (4.5 ECTS), WS 2017/18
Univ. Ass. Dipl.-Ing. Rainer Kelz
Ass. Prof. Dr. Andreas Arzt
Univ. Ass. Dipl.-Ing. Matthias Dorfer
Time: Thursday, 12:00 - 13:30
Start: Thursday, Oct. 5, 2017
Location: KEP 3
This class will be taught in English.
Class Objectives and Content
The lecture gives an introduction to the basics of reinforcement learning and covers
the following topics:
- Introduction: Definition of the Reinforcement Learning Problem
- Introduction: Scientific Computing with Python
- Multi-arm Bandits
- Finite Markov Decision Processes
- Dynamic Programming
- Monte Carlo Methods
- Temporal-Difference Learning
- Policy Gradient Methods
- Deep Reinforcement Learning (A Selection)
- Selected Applications and Examples of Reinforcement Learning
The content of this lecture is mainly based on the book: "Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
A draft of the book should be available here
There will also be complementary practical exercieses to improve the understanding of the material tought in the class in the form of homework assignments.
The assignments consist of analytical problems and programming exercises. Programming exercises will be solved using the Python programming language (no prior knowledge is required, there will be a short introduction in the first lesson).
Questions, suggestions, complaints, etc.
Tel. 0732 2468 4714
last edited by md on Aug 28, 2017