39 citations · 63 across the 21 of their papers we have counts for
3 papers · 1 filter
Safe Reinforcement Learning with Probabilistic Guarantees Satisfying Temporal Logic Specifications in Continuous Action Spaces
Hanna Krasowski, Prithvi Akella, Aaron D. Ames +1
Vanilla Reinforcement Learning (RL) can efficiently solve complex tasks but does not provide any guarantees on system behavior. To bridge this gap, we propose a three-step safe RL…
Provably Safe Reinforcement Learning via Action Projection using Reachability Analysis and Polynomial Zonotopes
Niklas Kochdumper, Hanna Krasowski, Xiao Wang +2
While reinforcement learning produces very promising results for many applications, its main disadvantage is the lack of safety guarantees, which prevents its use in safety-critica…
Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarking
Hanna Krasowski, Jakob Thumm, Marlon Müller +3
Ensuring the safety of reinforcement learning (RL) algorithms is crucial to unlock their potential for many real-world tasks. However, vanilla RL and most safe RL approaches do not…