In recent years, Reinforcement Learning (RL) has produced some of the most impressive results in the realm of Machine Learning (ML), especially in game playing (as with the game of Go) and robotics (e.g. RoboCup or autonomously navigating robots). Its view of the ML model as an agent acting within an environment, allows for learning by trial and error and therefore reasoning beyond human expert knowledge. RL is a quickly evolving field with new algorithms and applications being developed constantly.
In this seminar we will focus on algorithms and state-of-the-art in the field of RL by discussing relevant publications.
We will discuss following topics (not exhaustive):
We strongly recommend that you know the foundations of machine learning in order to attend the course. You should have attended the RL lecture!
The course will be held in English. We will have weekly sessions in which one or two students will present a paper each (in English). The paper can be any of those from the paper list. After the presentation you will discuss the paper in small groups and afterwards briefly together in the plenary.
So in total, you will be expected to
Your grade will consist of your presentation and the scientific report. The scientific report is a written discussion of the paper you chose, results of the discussion of the small group and the plenary best to be included.
In the end, you will
The full list of papers can be found on the StudIP course page.