Reinforcement Learning: Insights and Applications
Reinforcement learning is a field of machine learning that focuses on developing algorithms that enable an agent by novel machine learning technologies to learn optimal strategies through interaction with its environment.
Fall Semester, 2-Day Block Course, MAS MTEC
Lecturer: Christa Chuchiero, Bastian Bergmann, Josef Teichmann
Reinforcement learning is a subfield of machine learning that focuses on developing algorithms that enable an agent to learn through trial and error by interacting with its environment. RL differs from other ML algorithms, e.g. supervised learning in not needing labelled input/output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead, the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge). The environment is typically stated in the form of a Markov decision process (MDP). In this course we will go through the main architecture of reinforcement learning and review some of its applications.
On day 1 of the 2-day course the concept of a Markov Decision Process (MDP), its value function and the Bellmann equation are introduced and discussed. Several classical and ML powered algorithms are introduced and showcases presented.
On Day 2 the concept of a partially observed Markov Decision Process is introduced. Aspects of Filtering and embedding partially observed Markov decision processes into the framework of MDPs are presented. Showcases from Robotics and Finance with an emphasis on the latter are presented in theory and applications.
An understanding of basic machine learning concepts is welcomed but not mandatory (e.g. you took the class “Fundamentals on ML for Executives” or “AI for Executives”). In the beginning of the course, we will do a short primer on mathematics and statistics and some fundamental aspects of machine learning. We will provide coding examples for those you would like to follow the code.