  We explore the impact of environmental conditions on the competency of
machine learning agents and how real-time competency assessments improve the
reliability of ML agents. We learn a representation of conditions which impact
the strategies and performance of the ML agent enabling determination of
actions the agent can make to maintain operator expectations in the case of a
convolutional neural network that leverages visual imagery to aid in the
obstacle avoidance task of a simulated self-driving vehicle.
