Risk-averse autonomous systems: A brief history and recent developments
  from the perspective of optimal control
  We offer a historical overview of methodologies for quantifying the notion of
risk and optimizing risk-aware autonomous systems, with emphasis on risk-averse
settings in which safety may be critical. We categorize and present
state-of-the-art approaches, and we describe connections between such
approaches and ideas from the fields of decision theory, operations research,
reinforcement learning, and stochastic control. The first part of the review
focuses on model-based risk-averse methods. The second part discusses methods
that blend model-based and model-free techniques for the purpose of designing
policies with improved adaptive capabilities. We conclude by highlighting areas
for future research.
