Advanced Topics in Reinforcement Learning and Planning
- Master Computer Science
- Master Data Science
- Master Media Informatics
- Master Software Systems Engineering
Students will present papers from a list of important, recent works in machine learning compiled by the professor. Topics include deep and reinforcement learning, and transformer and GNN architectures. The focus will be on the use of these techniques in simple applications that are crisp and easy to understand. The list of papers will also include those underlying well-known learning systems like ChaptGPT, AlphaZero, Gato, etc. The final list of papers and topics to be covered will be defined after the first meeting.
Knowledge: on completion, the students have acquired detailed knowledge on
- models and algorithms for planning,
- models and algorithms for reinforcement learning, and
- models and algorithms for reinforcement learning that use deep neural networks.
Skills: on completion, the students know
- how these various algorithms work
- when they can be applied, and
- how they can be applied
Competences: on completion, the students are able to
- model and solve sequential decision problems,
- formulate and solve sequential decision problems by trial and error when model is not known
- formulate and solve sequential decision problems over large state states using deep nets.
Recommended prior knowledge
Bachelor degree in CS or equivalent and basic AI and ML courses, with some previous exposure to deep learning and reinforcement learning.