• Markov Decision Process and Risk Project
    May 2019 - April 2020
    FAPESP grant 2018/11236-9
    Abstract: Markov Decision Processes (MDPs) are widely used to solve sequential decision-making problems. The most used criterion in this type of problem to find a solution is to minimize the expected total cost. However, this approach does not take into account cost variability (that is, fluctuations around the average), which can significantly affect the performance of the solution. MDPs that deal with these types of problems are called risk-sensitive MDPs. Among the risk-sensitive MDPs we have: (i) MDPs that use as an optimization criterion the expected exponential utility; (ii) MDPs whose objective is to maximize the probability that the accumulated cost is not greater than a given limit provided by the user, called MDP with limited budget; (iii) MDPs whose criteria include the CVaR metric, a robust way of measuring risk commonly used in the financial area, called CVaR MDPs; and (iv) MDPs whose criterion uses the average of the total cost in conjunction with the CVaR criterion, called mean-CVAR MDPs. This research project intends to work with MDPs with limited budget, CVar MDPs and mean-CVaR MDPs. The main objective is to propose exact and approximate algorithms based on dynamic programming to solve these risk-sensitive MDPs.
    Coordinator: Karina Valdivia Delgado.
    Project page

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