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.