In this project, students will use popular reinforcement learning (RL) frameworks to train agents to solve a realistic industrial application with the "eXtended Transport System" (XTS).
The focus of this project lies in the in training of RL agents for the XTS regarding energy optimality, given an energy buffer in the system. The aim is to orchestrate the individual agents in a way to reach better energy consumptions while maintaining scalability of the system.
At the end, there is the option of running the agents on a real XTS in our lab.
| Rhythmus | Tag | Uhrzeit | Format / Ort | Zeitraum | |
|---|---|---|---|---|---|
| by appointment | n.V. | 13.04.-24.07.2026 |
| Modul | Veranstaltung | Leistungen | |
|---|---|---|---|
| 39-M-Inf-P Project Projekt | Projekt | unbenotete Prüfungsleistung
|
Studieninformation |
| 39-M-Inf-P_NWI_a Project Projekt | Projekt | unbenotete Prüfungsleistung
|
Studieninformation |
Die verbindlichen Modulbeschreibungen enthalten weitere Informationen, auch zu den "Leistungen" und ihren Anforderungen. Sind mehrere "Leistungsformen" möglich, entscheiden die jeweiligen Lehrenden darüber.