392205 Project: RL for Multi-Agent Energy Optimization (Pj) (SoSe 2026)
Contents, comment
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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.
Teaching staff
Dates ( Calendar view )
| Frequency | Weekday | Time | Format / Place | Period | |
|---|---|---|---|---|---|
| by appointment | n.V. | 13.04.-24.07.2026 |
Subject assignments
| Module | Course | Requirements | |
|---|---|---|---|
| 39-M-Inf-P Project Projekt | Projekt | Ungraded examination
|
Student information |
| 39-M-Inf-P_NWI_a Project Projekt | Projekt | Ungraded examination
|
Student information |
The binding module descriptions contain further information, including specifications on the "types of assignments" students need to complete. In cases where a module description mentions more than one kind of assignment, the respective member of the teaching staff will decide which task(s) they assign the students.
- Address:
- SS2026_392205@ekvv.uni-bielefeld.de
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- Notes:
- Additional notes on the electronic mailing lists
- Last update basic details/teaching staff:
- Tuesday, April 28, 2026
- Last update times:
- Tuesday, April 28, 2026
- Last update rooms:
- Tuesday, April 28, 2026
- Type(s) / SWS (hours per week per semester)
- project (Pj) / 2
- Language
- This lecture is taught in english
- Department
- Faculty of Technology
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