119 citations · 136 across the 14 of their papers we have counts for
4 papers · 1 filter
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…
Reinforcement Learning under Partial Observability Guided by Learned Environment Models
Edi Muskardin, Martin Tappler, Bernhard K. Aichernig +1
In practical applications, we can rarely assume full observability of a system's environment, despite such knowledge being important for determining a reactive control system's pre…
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…