  Groundbreaking successes have been achieved by Deep Reinforcement Learning
(DRL) in solving practical decision-making problems. Robotics, in particular,
can involve high-cost hardware and human interactions. Hence, scrupulous
evaluations of trained models are required to avoid unsafe behaviours in the
operational environment. However, designing metrics to measure the safety of a
neural network is an open problem, since standard evaluation parameters (e.g.,
total reward) are not informative enough. In this paper, we present a
semi-formal verification approach for decision-making tasks, based on interval
analysis, that addresses the computational demanding of previous verification
frameworks and design metrics to measure the safety of the models. Our method
obtains comparable results over standard benchmarks with respect to formal
verifiers, while drastically reducing the computation time. Moreover, our
approach allows to efficiently evaluate safety properties for decision-making
models in practical applications such as mapless navigation for mobile robots
and trajectory generation for manipulators.
