My Research field is Reinforcement Learning for Revenue Management I investigate improvements regarding stability and data efficiency of reinforcement learning algorithms when used on economic problems in order to allow the practical application of those tools even when strong constraints regarding the available information have to be considered.
Groeneveld, J., Herrmann, J., Mollenhauer, N., Dreessen, L., Bessin, N., Schulze-Tast, J., Kastius, A., Huegle, J., Schlosser, R.: Self-Learning Agents for Recommerce Markets. Business & Information Systems Engineering, accepted. (2023).
Kossmann, J., Kastius, A., Schlosser, R.: SWIRL: Selection of Workload-aware Indexes using Reinforcement Learning. 25th International Conference on Extending Database Technology (EDBT 2022). pp. 155–168 (2022).
Our research group investigates both the use of energy in developing artificial intelligence (AI) as well as the use of AI in generating, storing and managing energy. This includes research into energy-efficient algorithms for solving basic AI tasks such as classification, ranking or planning & search, as well as the development and application of AI methods to refined modeling of batteries in order to extend their working lifetime, and the control of domestic energy consumption.