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cs.LG2026
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…
cs.LG2026
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…
cs.LG2025
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…