12 citations · 12 across the 3 of their papers we have counts for
7 papers
Online Shielding for Reinforcement Learning
Bettina Könighofer, Julian Rudolf, Alexander Palmisano +2
Besides the recent impressive results on reinforcement learning (RL), safety is still one of the major research challenges in RL. RL is a machine-learning approach to determine nea…
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…
Online Shielding for Stochastic Systems
Bettina Könighofer, Julian Rudolf, Alexander Palmisano +2
In this paper, we propose a method to develop trustworthy reinforcement learning systems. To ensure safety especially during exploration, we automatically synthesize a correct-by-c…
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…
Synthesis of Admissible Shields
Laura Humphrey, Bettina Könighofer, Robert Könighofer +1
Shield synthesis is an approach to enforce a set of safety-critical properties of a reactive system at runtime. A shield monitors the system and corrects any erroneous output value…
Safe Reinforcement Learning via Shielding
Mohammed Alshiekh, Roderick Bloem, Ruediger Ehlers +3
Reinforcement learning algorithms discover policies that maximize reward, but do not necessarily guarantee safety during learning or execution phases. We introduce a new approach t…