2 citations · 2 across the 6 of their papers we have counts for
7 papers
Explainably Safe Reinforcement Learning
Sabine Rieder, Stefan Pranger, Debraj Chakraborty +2
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, whos…
Easy-to-Use Shielding for Reinforcement Learning
Stefan Pranger, Bettina Könighofer
Safe exploration is a key challenge in Reinforcement Learning (RL) that aims to prevent agents from making harmful decisions while exploring their environment. Safe exploration is…
Test Where Decisions Matter: Importance-driven Testing for Deep Reinforcement Learning
Stefan Pranger, Hana Chockler, Martin Tappler +1
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 v…
Tools at the Frontiers of Quantitative Verification
Roman Andriushchenko, Alexander Bork, Carlos E. Budde +20
The analysis of formal models that include quantitative aspects such as timing or probabilistic choices is performed by quantitative verification tools. Broad and mature tool suppo…
Automata Learning meets Shielding
Martin Tappler, Stefan Pranger, Bettina Könighofer +3
Safety is still one of the major research challenges in reinforcement learning (RL). In this paper, we address the problem of how to avoid safety violations of RL agents during exp…
TEMPEST -- Synthesis Tool for Reactive Systems and Shields in Probabilistic Environments
Stefan Pranger, Bettina Könighofer, Lukas Posch +1
We present Tempest, a synthesis tool to automatically create correct-by-construction reactive systems and shields from qualitative or quantitative specifications in probabilistic e…
Adaptive Shielding under Uncertainty
Stefan Pranger, Bettina Könighofer, Martin Tappler +3
This paper targets control problems that exhibit specific safety and performance requirements. In particular, the aim is to ensure that an agent, operating under uncertainty, will…