Formally Verifying Deep Reinforcement Learning Controllers with Lyapunov Barrier Certificates
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 …
agents that control autonomous systems. However, the" black box" nature of DRL agents …
Shield Synthesis for LTL Modulo Theories
In recent years, Machine Learning (ML) models have achieved remarkable success in
various domains. However, these models also tend to demonstrate unsafe behaviors …
various domains. However, these models also tend to demonstrate unsafe behaviors …
Verification-Guided Shielding for Deep Reinforcement Learning
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 …
to solving real-world tasks. However, despite their successes, DRL-based policies suffer …
Verifying the Generalization of Deep Learning to Out-of-Distribution Domains
Deep neural networks (DNNs) play a crucial role in the field of machine learning,
demonstrating state-of-the-art performance across various application domains. However …
demonstrating state-of-the-art performance across various application domains. However …
Safe and Reliable Training of Learning-Based Aerospace Controllers
In recent years, deep reinforcement learning (DRL) approaches have generated highly
successful controllers for a myriad of complex domains. However, the opaque nature of …
successful controllers for a myriad of complex domains. However, the opaque nature of …
Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning
An exciting and promising frontier for Deep Reinforcement Learning (DRL) is its application
to real-world robotic systems. While modern DRL approaches achieved remarkable …
to real-world robotic systems. While modern DRL approaches achieved remarkable …