Name
Title | ||
|---|---|---|
Knowledge-based generation of a plant-specific reinforcement learning framework for energy reduction of production plants. |
Abstract | ||
|---|---|---|
In research, there are more and more successful approaches to operate production plants more resource-efficiently and more productively with the help of reinforcement learning. An important point is the reduction of the energy demand of production plants. Instead of manually implementing complex standby strategies rigidly in a PLC, an intelligent system can train to derive decisions about the optimal energetic state of each component autonomously. Since learning in a virtual environment has decisive advantages, simulation models with sufficient accuracy are necessary. Many of the previous implementations of reinforcement learning approaches in an industrial environment are usually tailored to a specific plant. In particular, the agent\u0027s scope of action and its connection to the environment must be adapted manually for a new plant. The presented solution shows an intelligent system which automatically optimizes the virtual learning environment with the support of plant knowledge and adapts the reinforcement learning agent to the respective production plant. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ETFA46521.2020.9211957 | ETFA |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elisabeth Schmidl | 1 | 0 | 0.34 |
| Eva Fischer | 2 | 0 | 0.34 |
| Matthias Wenk | 3 | 0 | 0.34 |
| Jörg Franke | 4 | 26 | 20.00 |