  Hierarchical Reinforcement Learning has been previously shown to speed up the
convergence rate of RL planning algorithms as well as mitigate feature-based
model misspecification (Mankowitz et. al. 2016a,b, Bacon 2015). To do so, it
utilizes hierarchical abstractions, also known as skills -- a type of
temporally extended action (Sutton et. al. 1999) to plan at a higher level,
abstracting away from the lower-level details. We incorporate risk sensitivity,
also referred to as Situational Awareness (SA), into hierarchical RL for the
first time by defining and learning risk aware skills in a Probabilistic Goal
Semi-Markov Decision Process (PG-SMDP). This is achieved using our novel
Situational Awareness by Risk-Conscious Skills (SARiCoS) algorithm which comes
with a theoretical convergence guarantee. We show in a RoboCup soccer domain
that the learned risk aware skills exhibit complex human behaviors such as
`time-wasting' in a soccer game. In addition, the learned risk aware skills are
able to mitigate reward-based model misspecification.
