3 papers
cs.LG2026
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…
cs.LG2026
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…
cs.LG2024
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…