Lecturer: Jim Mainprice
TAs: Philipp Kratzer, Yoojin Oh, Janik Hager
All material of this course is available via ilias: Course on ilias
ON JUNE 21: THE LECTURE WILL BE RECORDED LATER IN THE EVENING
TIME CHANGE: LECTURES ON MONDAY ARE NOW AT LIVE 15h45
The lecture will be held using a LIVE STREAM online on the normal lecture slots on the following youtube channel:
There will be a possibility for questions through comments on the live stream. For later access, the videos from the live stream will be uploaded to ilias. The tutorials will be held using Webex, the link and the password is available in ilias.
- Lectures on Mondays 15:45
- Tutorials on Tuesdays 11:30 (group 1) and 14:00 (group 2)
Reinforcement Learning is one of the most active research areas in artificial intelligence. It aims to find an optimal policy to achieve a goal by interacting with a complex, uncertain environment – in absence of explicit teachers. Recent success of Reinforcement Learning include mastering the game of GO or learning to play Atari games from raw pixel input. Moreover, there exist various applications in other fields, such as finance, robotics, …
As a prerequisite, students should have knowledge in math (linear algebra, multivariable calculus, statistics, probability theory) and programming (typical programming structures, basic algorithms, object-oriented programming). Coding experience in python is helpful for the exercises. If you are new to python with numpy, we recommend the quickstart.