Formal verification of neural networks for safety-critical tasks in deep reinforcement learning

Abstract

In the last years, neural networks achieved groundbreaking successes in a wide variety of applications. However, for safety critical tasks, such as robotics and healthcare, it is necessary to provide some specific guarantees before the deployment in a real world context. Even in these scenarios, where high cost equipment and human safety are involved, the evaluation of the models is usually performed with the standard metrics (i.e., cumulative reward or success rate). In this paper, we introduce a novel metric for the evaluation of models in safety critical tasks, the