|Lecturer:||Sepp Hochreiter, Jose Arjona, Michael Gillhofer|
|Times/locations:||Mon 12:45-13:30, room K 224B|
|Registration:||KUSSS, opens an external URL in a new window|
Reinforcement Learning is getting more and more relevance in the field of Machine Learning, playing a fundamental role in a wide range of areas such as autonomous driving, robotics or health-care. Classical reinforcement learning techniques combined with Deep Learning allow for complex systems able to perform highly sophisticated tasks, unthinkable only one decade ago.
This course will provide a broad view of the most important state-of-the-art methods and the core challenges that Reinforcement Learning is facing nowadays. The goal of this course is to assimilate the key ideas and understand upcoming contributions to the field.
The lectures are distributed in three main blocks: introduction, basic methods and advance methods. Additionally, a lecture about how to evaluate RL methods is introduced since it is a research topic itself. As a final lecture, an analysis of the latest major keystone in the field, AlphaStar is analysed and discussed.
Evaluation in Reinforcement Learning
A case study: Alphasta