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Topic AssignmentWritten on 28.04.26 by Magnus Cunow Dear Students, We have assigned you a topic that you can find on your personal status page. In addition to your topic, you will find your presentation date and supervisor. We strongly recommend contacting your supervisor early if you have any questions about the paper or would like to discuss how… Read more Dear Students, We have assigned you a topic that you can find on your personal status page. In addition to your topic, you will find your presentation date and supervisor. We strongly recommend contacting your supervisor early if you have any questions about the paper or would like to discuss how to structure your presentation. Please send your slides to your supervisor at least one week in advance to receive feedback. Furthermore, you must register in LSF by 19.05.2026 at the latest, in accordance with examination office regulations. Please note that once the registration deadline has passed, we will not be able to resolve this. See you on 12.06 at 8:30, room TBA. If you have any questions, please do not hesitate to contact us. Best regards, |
Kick-off Slides and Topic SelectionWritten on 21.04.26 by Magnus Cunow Dear students, thank you for attending the kick-off session. The kick-off slides are now available under Materials in the CMS. Please find the list of topics, including links to the corresponding papers, under Topics. You can use this list to get a quick overview and decide which topics… Read more Dear students, thank you for attending the kick-off session. The kick-off slides are now available under Materials in the CMS. Please find the list of topics, including links to the corresponding papers, under Topics. You can use this list to get a quick overview and decide which topics interest you most. Please indicate your favored topics and least favored (blocked) topics by April 26, 23:59 on your personal status page. If anything is unclear, feel free to reach out. Best, |
Special Topics in Deep Reinforcement Learning
Reinforcement Learning (RL) is a popular sub-discipline of Machine Learning for problems that require strategies to solve complex tasks such as board games, scheduling problems, or other discrete optimization problems. Deep Reinforcement Learning (DRL) is the combination of Reinforcement Learning with Neural Networks.
In this seminar, we will first cover the most important algorithms of Deep Reinforcement Learning. Afterwards, we will dive deeper and have a look on special topics that our group is conducting research on. In particular, we will consider exploration techniques, agentic RL, and AI planning.
The kick-off will take place on 21.04.26, 13:00 at DFKI D3.4 in room Reuse (-2.17). Participation is mandatory.