Deep Neural Networks are increasingly adopted in critical tasks that require a high level of
safety, eg, autonomous driving. While state-of-the-art verifiers can be employed to check …

Cost functions are commonly employed in Safe Deep Reinforcement Learning (DRL).
However, the cost is typically encoded as an indicator function due to the difficulty of …

Safety is essential for deploying Deep Reinforcement Learning (DRL) algorithms in real-
world scenarios. Recently, verification approaches have been proposed to allow quantifying …

Identifying safe areas is a key point to guarantee trust for systems that are based on Deep
Neural Networks (DNNs). To this end, we introduce the AllDNN-Verification problem: given a …

Safety is critical to broadening the application of reinforcement learning (RL). Often, we train
RL agents in a controlled environment, such as a laboratory, before deploying them in the …

Traditional visual navigation methods of micro aerial vehicle (MAV) usually calculate a
passable path that satisfies the constraints depending on a prior map. However, these …

Underwater or planetary exploration are prime examples of missions that can benefit from
autonomous agents working together. However, discovering effective team-level behaviors …

Safety is critical to broadening the application of reinforcement learning (RL). Often, RL
agents are trained in a controlled environment, such as a laboratory, before being deployed …

Deep Neural Networks (DNNs) are powerful tools that have shown extraordinary results in
many scenarios, ranging from pattern recognition to complex robotic problems. However …

Deep Neural Networks (DNN) are crucial in approximating nonlinear functions across
diverse applications, ranging from image classification to control. Verifying specific input …