Processos de decisão de Markov com sensibilidade a risco com função de utilidade exponencial: Uma revisão sistemática da literatura

Abstract

Markov Decision Process (MDP) has been used very eciently to solve sequential decision-making problems. There are problems in which dealing with the risks of the environment to obtain a reliable result is more important than maximizing the expected average return. MDPs that deal with this type of problem are called risk-sensitive Markov decision processes (RSMDP). This systematic review of the literature aims to identify the theoretical results and proposed algorithms to solve RSMDP problems that have an exponential utility function, evaluating their main characteristics, similarities, particularities and differences in order to allow the reader the knowledge of this tool of decision making for risk sensitive problems.