Analysis of the Risk Sensitive Value Iteration Algorithm - Borges, Igor Oliveira; Delgado, Karina Valdivia; Freire, Valdinei (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2018: Anais do XV Encontro Nacional de Inteligência Artificial e ComputacionalDO - 10.5753/eniac.2018.4431, 2018)
This paper shows an empirical study of Value Iteration Risk Sensitive algorithm proposed by Mihatsch and Neuneier (2002). This approach makes use of a risk factor that allows dealing with different types of risk attitude (prone, neutral or averse) by using a discount factor. We show experiments with the domain of Crossing the River in two different scenarios and we analyze the influence of discount factor and risk factor under two aspects: optimal policy and processing time to convergence. We observed that: (i) the processing cost in extreme risk policies is high with both risk-averse and risk-prone attitude; (ii) a high discount increases time to convergence and reinforces the chosen risk attitude; and (iii) policies with intermediate risk factor values have a low computational cost and show a certain sensitivity to risk based on the discount factor.