7 citations · 11 across the 5 of their papers we have counts for
5 papers
Safe Reinforcement Learning using Action Projection: Safeguard the Policy or the Environment?
Hannah Markgraf, Shambhuraj Sawant, Hanna Krasowski +3
Projection-based safety filters, which modify unsafe actions by mapping them to the closest safe alternative, are widely used to enforce safety constraints in reinforcement learnin…
Leveraging Analytic Gradients in Provably Safe Reinforcement Learning
Tim Walter, Hannah Markgraf, Jonathan Külz +1
The deployment of autonomous robots in safety-critical applications requires safety guarantees. Provably safe reinforcement learning is an active field of research that aims to pro…
PyTupli: A Scalable Infrastructure for Collaborative Offline Reinforcement Learning Projects
Hannah Markgraf, Michael Eichelbeck, Daria Cappey +3
Offline reinforcement learning (RL) has gained traction as a powerful paradigm for learning control policies from pre-collected data, eliminating the need for costly or risky onlin…
CommonPower: A Framework for Safe Data-Driven Smart Grid Control
Michael Eichelbeck, Hannah Markgraf, Matthias Althoff
The growing complexity of power system management has led to an increased interest in reinforcement learning (RL). To validate their effectiveness, RL algorithms have to be evaluat…
Contingency-constrained economic dispatch with safe reinforcement learning
Michael Eichelbeck, Hannah Markgraf, Matthias Althoff
Future power systems will rely heavily on micro grids with a high share of decentralised renewable energy sources and energy storage systems. The high complexity and uncertainty in…