
All the data created for the publication "Learning the Optimal Power Flow: Environment Design Matters" by Wolgast and Nieße. The dataset contains all training runs performed, including the final neural network weights, meta-data about the training run, and various metrics during the course of training, which were used to generate the results and plots. The source code to re-produce the plots for the publication (and everything else) can be found on GitHub: https://github.com/Digitalized-Energy-Systems/rl-opf-env-design
Machine learning, Power engineering, Reinforcement learning, Environment design, Optimal power flow
Machine learning, Power engineering, Reinforcement learning, Environment design, Optimal power flow
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
