  Deep neural networks (DNNs) are the workhorses of deep learning, which
constitutes the state of the art in numerous application domains. However,
DNN-based decision rules are notoriously prone to poor generalization, i.e.,
may prove inadequate on inputs not encountered during training. This limitation
poses a significant obstacle to employing deep learning for mission-critical
tasks, and also in real-world environments that exhibit high variability. We
propose a novel, verification-driven methodology for identifying DNN-based
decision rules that generalize well to new input domains. Our approach
quantifies generalization to an input domain by the extent to which decisions
reached by independently trained DNNs are in agreement for inputs in this
domain. We show how, by harnessing the power of DNN verification, our approach
can be efficiently and effectively realized. We evaluate our verification-based
approach on three deep reinforcement learning (DRL) benchmarks, including a
system for Internet congestion control. Our results establish the usefulness of
our approach. More broadly, our work puts forth a novel objective for formal
verification, with the potential for mitigating the risks associated with
deploying DNN-based systems in the wild.
