Coding Lab: Deep Reinforcement Learning (SoSe)

Deep Reinforcement Learning has recently evolved as a vivid field of research that seeps into various industries and applications. In this seminar, we build on the theoretical foundations that we learned in the course "Introduction to Deep Reinforcement Learning" and study a real-world application case in order to understand how to design and implement advanced DRL algorithms to solve use cases of practical interest.

To participate in this course, you have to enroll via TUMonline.

Mandatory Prerequisites

• Profound programming skills and at least intermediate knowledge in Python
• Passed exam of Introduction to Deep Reinforcement Learning (MGT001299)

Intended Learning Outcomes

The objective of this seminar is to provide the students with the necessary skills to conduct independent research in the field of Deep Reinforcement Learning, e.g., in preparation for a successful master thesis or a research stay abroad. Herein, students learn how to i) structure a research question and conduct a literature review, ii) use and develop adequate models, software, and algorithms to solve the research question, iii) write a research paper, especially how to structure and organize results and the corresponding methodology, and iv) work in an academic environment where knowledge is mostly gained from recent papers and less from standardized books.

Teaching and Learning Method

This seminar bases on various methods: First, the lecturer gives an introduction into the general topic and an overview on recent trends and challenges in an introductory session. Afterward, a case study based on a real-world application is introduced. Then, students will work in small groups on this case study, using and practicing their skills in literature research, mathematical modeling, and programming. During this phase, the students receive weekly guidance and feedback from the lecturer. Furthermore, this work phase is structured through specific milestones, e.g., discussing a preliminary outline, first results, and main findings. Finally, the students create a final paper on their work and presentations as well as discussions of all papers take place such that the students reflect their work and train their presentation skills.

Course Criteria & Registration

When you register for the course, the system will put you on the wait-list. Additionally, please send your CV and transcript to kai.jungel@tum.de. Your transcript must show the grade from the lecture "Introduction to Deep Reinforcement Learning". If you took the exam for the lecture in February 2024 and the grade is not shown on your transcript yet, please state that in your e-mail (you can of course still register in this case). After the registration deadline, in the week of April 8, we will review your documents and assign the 21 available spots based on your application.