8 citations · 37 across the 26 of their papers we have counts for
4 papers · 1 filter
Safe and Reliable Training of Learning-Based Aerospace Controllers
Udayan Mandal, Guy Amir, Haoze Wu +10
In recent years, deep reinforcement learning (DRL) approaches have generated highly successful controllers for a myriad of complex domains. However, the opaque nature of these mode…
Formally Verifying Deep Reinforcement Learning Controllers with Lyapunov Barrier Certificates
Udayan Mandal, Guy Amir, Haoze Wu +9
Deep reinforcement learning (DRL) is a powerful machine learning paradigm for generating agents that control autonomous systems. However, the ``black box'' nature of DRL agents lim…
Marabou 2.0: A Versatile Formal Analyzer of Neural Networks
Haoze Wu, Omri Isac, Aleksandar Zeljić +14
This paper serves as a comprehensive system description of version 2.0 of the Marabou framework for formal analysis of neural networks. We discuss the tool's architectural design a…
Formally Explaining Neural Networks within Reactive Systems
Shahaf Bassan, Guy Amir, Davide Corsi +2
Deep neural networks (DNNs) are increasingly being used as controllers in reactive systems. However, DNNs are highly opaque, which renders it difficult to explain and justify their…