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Stefan Pranger

3 accepted papers

2026

Efficient and Safe Molecular Assembly via Reinforcement Learning and Constraint Solving

ICML 2026poster

Scanning tunneling microscopy (STM) enables precise manipulation of individual atoms and molecules, offering a pathway to constructing nanoscale assemblies with rich quantum mechanical behavior. Despite its potential, STM-based fabrication remains limited by the inherent complexity of manipulation p…

Cited by 0SourceScholar
2025

Explainably Safe Reinforcement Learning

NeurIPS 2025poster

Trust in a decision-making system requires both safety guarantees and the ability to interpret and understand its behavior. This is particularly important for learned systems, whose decision-making processes are often highly opaque. Shielding is a prominent model-based technique for enforcing safety…

Cited by 0SourceScholar
2024

Test Where Decisions Matter: Importance-driven Testing for Deep Reinforcement Learning

NeurIPS 2024poster

In many Deep Reinforcement Learning (RL) problems, decisions in a trained policy vary in significance for the expected safety and performance of the policy. Since RL policies are very complex, testing efforts should concentrate on states in which the agent's decisions have the highest impact on the…

Cited by 0SourcePDFScholar