[ 536MLPEREIK19 ] KV Reinforcement Learning

Workload Education level Study areas Responsible person Hours per week Coordinating university
4,5 ECTS B3 - Bachelor's programme 3. year Computer Science Gerhard Widmer 3 hpw Johannes Kepler University Linz
Detailed information
Original study plan Bachelor's programme Artificial Intelligence 2019W
Objectives Introduction to basic principles and techniques of Reinforcement Learning.
Subject Defining the Reinforcement Learning problem relative to supervised and unsupervised machine learning. Defining k-armed Bandits, contextual bandits, and finally the Markov Decision Process (MDP). Formalizing the RL problem. Basic table-based algorithms to explore the space of possible solution methods. Introduction of more useful, advanced, but approximate solution methods. Examples of state-of-the-art methods, including deep Reinforcement Learning.
Criteria for evaluation Multiple programming exercises in Python throughout the semester. Written exam at the end of the semester.
Methods Slide presentation, discussion of common problems during the exercise.
Language English
Study material Slides used in the lecture will be made available via Moodle.
Changing subject? No
Further information R. Sutton and A. Barto (2018). Reinforcement Learning: An Introduction. (Second Edition). Cambridge, MA: The MIT Press.
On-site course
Maximum number of participants -
Assignment procedure Direct assignment