2 citations · 2 across the 6 of their papers we have counts for
6 papers
Explanations for Unrealizability of Infinite-State Safety Shields
Andoni Rodriguez, Irfansha Shaik, Davide Corsi +2
Safe Reinforcement Learning focuses on developing optimal policies while ensuring safety. A popular method to address such task is shielding, in which a correct-by-construction saf…
Efficient Dynamic Shielding for Parametric Safety Specifications
Davide Corsi, Kaushik Mallik, Andoni Rodriguez +1
Shielding has emerged as a promising approach for ensuring safety of AI-controlled autonomous systems. The algorithmic goal is to compute a shield, which is a runtime safety enforc…
Realizable Continuous-Space Shields for Safe Reinforcement Learning
Kyungmin Kim, Davide Corsi, Andoni Rodriguez +5
While Deep Reinforcement Learning (DRL) has achieved remarkable success across various domains, it remains vulnerable to occasional catastrophic failures without additional safegua…
Verification-Guided Shielding for Deep Reinforcement Learning
Davide Corsi, Guy Amir, Andoni Rodriguez +3
In recent years, Deep Reinforcement Learning (DRL) has emerged as an effective approach to solving real-world tasks. However, despite their successes, DRL-based policies suffer fro…
Shield Synthesis for LTL Modulo Theories
Andoni Rodriguez, Guy Amir, Davide Corsi +2
In recent years, Machine Learning (ML) models have achieved remarkable success in various domains. However, these models also tend to demonstrate unsafe behaviors, precluding their…
From Realizability Modulo Theories to Synthesis Modulo Theories Part 1: Dynamic approach
Andoni Rodríguez, Cesar Sanchez
Reactive synthesis is the process of using temporal logic specifications in LTL to generate correct controllers, but its use has been restricted to Boolean specifications. Recently…