119 citations · 122 across the 5 of their papers we have counts for
9 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…
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…
Search-Based Testing of Reinforcement Learning
Martin Tappler, Filip Cano Córdoba, Bernhard K. Aichernig +1
Evaluation of deep reinforcement learning (RL) is inherently challenging. Especially the opaqueness of learned policies and the stochastic nature of both agents and environments ma…
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…
Learning a Behavior Model of Hybrid Systems Through Combining Model-Based Testing and Machine Learning (Full Version)
Bernhard K. Aichernig, Roderick Bloem, Masoud Ebrahimi +6
Models play an essential role in the design process of cyber-physical systems. They form the basis for simulation and analysis and help in identifying design problems as early as p…