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.

翻译：受可变环境影响的自主系统急剧增加,导致迫切需要在综合和核实这些系统的政策时考虑风险。本文件旨在解决与风险意识核查和政策综合有关的几个问题,首先开发一种基于样本的方法,将一个分布不明的随机变量的风险计量评价捆绑起来。这些界限使我们能够为一大批机器人系统生成高度自信的核查报表。第二,我们开发一种基于样本的方法,以确定非通货优化问题的解决办法,这些问题比可能的解决办法的决策空间的很大一部分要强。两种基于样本的方法都使我们能够迅速综合出风险意识政策,保证达到最低限度的系统性能。为了在模拟中展示我们的方法,我们核查一个合作的多试剂系统,并开发出一种超出系统基线控制器的风险意识控制器。我们还提及如何扩大我们的方法,以计算出任何价值g-亚热带风险措施,即我们集中关注的一致风险措施。