  The dramatic increase of autonomous systems subject to variable environments
has given rise to the pressing need to consider risk in both the synthesis and
verification of policies for these systems. This paper aims to address a few
problems regarding risk-aware verification and policy synthesis, by first
developing a sample-based method to bound the risk measure evaluation of a
random variable whose distribution is unknown. These bounds permit us to
generate high-confidence verification statements for a large class of robotic
systems. Second, we develop a sample-based method to determine solutions to
non-convex optimization problems that outperform a large fraction of the
decision space of possible solutions. Both sample-based approaches then permit
us to rapidly synthesize risk-aware policies that are guaranteed to achieve a
minimum level of system performance. To showcase our approach in simulation, we
verify a cooperative multi-agent system and develop a risk-aware controller
that outperforms the system's baseline controller. We also mention how our
approach can be extended to account for any $g$-entropic risk measure - the
subset of coherent risk measures on which we focus.
