  Safe reinforcement learning deals with mitigating or avoiding unsafe
situations by reinforcement learning (RL) agents. Safe RL approaches are based
on specific risk representations for particular problems or domains. In order
to analyze agent behaviors, compare safe RL approaches, and effectively
transfer techniques between application domains, it is necessary to understand
the types of risk specific to safe RL problems. We performed a systematic
literature mapping with the objective to characterize risk in safe RL. Based on
the obtained results, we present definitions, characteristics, and types of
risk that hold on multiple application domains. Our literature mapping covers
literature from the last 5 years (2017-2022), from a variety of knowledge areas
(AI, finance, engineering, medicine) where RL approaches emphasize risk
representation and management. Our mapping covers 72 papers filtered
systematically from over thousands of papers on the topic. Our proposed notion
of risk covers a variety of representations, disciplinary differences, common
training exercises, and types of techniques. We encourage researchers to
include explicit and detailed accounts of risk in future safe RL research
reports, using this mapping as a starting point. With this information,
researchers and practitioners could draw stronger conclusions on the
effectiveness of techniques on different problems.

Machine Learning