  In recent years, deep reinforcement learning (DRL) approaches have generated
highly successful controllers for a myriad of complex domains. However, the
opaque nature of these models limits their applicability in aerospace systems
and safety-critical domains, in which a single mistake can have dire
consequences. In this paper, we present novel advancements in both the training
and verification of DRL controllers, which can help ensure their safe behavior.
We showcase a design-for-verification approach utilizing k-induction and
demonstrate its use in verifying liveness properties. In addition, we also give
a brief overview of neural Lyapunov Barrier certificates and summarize their
capabilities on a case study. Finally, we describe several other novel
reachability-based approaches which, despite failing to provide guarantees of
interest, could be effective for verification of other DRL systems, and could
be of further interest to the community.
