Seminar (Lehre)

This course is a basic introduction to Deep Reinforcement Learning (RL). In RL, an agent learns to make sequential decisions by interacting with an environment to maximize some notion of reward. Deep RL combines RL and deep learning, in that neural networks are used to represent the agent's value functions or decision making policies, enabling the handling of complex input spaces such as images or sensor readings. This approach has led to significant advancements in tackling problems such as playing video games, robotics control, and autonomous driving. As a result, expertise in RL constitutes a significant advantage in the industrial job market. By the end of the seminar, it is expected that students will gain proficiency in designing their own RL algorithms, enabling them to apply it to different areas such as robotics, recommendation systems, gaming, etc. to name a few, and also comprehend current literature in the field.

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SoSe 24

Di. 14:00 - 15:30 (wöchentlich)
Erster Termin:Dienstag, 09.04.2024 14:00 - 15:30

As a prerequisite, this seminar will assume familiarity with probability, linear algebra, and programming in Python.

The seminar is divided into six blocks (following an introductory session), each lasting two weeks. Every block consists of the following components: * Week 1: Preparation of a presentation using prescribed sources (book chapters, video lectures, scientific articles). * Week 2: Presentations by 2 participants, each lasting 25 minutes based on a topic assigned to each participant in the first session of the seminar.