Robust topological policy iteration for infinite horizon bounded Markov Decision Processes

Autor(es)

Silva Reis, Willy Arthur ^[1] ; de Barros, Leliane Nunes ^[1] ; Delgado, Karina Valdivia ^[2]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Inst Math & Stat, R Matao 1010, Sao Paulo - Brazil

^[2] Univ Sao Paulo, Sch Arts Sci & Humanities, Av Arlindo Bettio 1000, Sao Paulo - Brazil

• Tipo de documento: Artigo Científico
• Fonte: INTERNATIONAL JOURNAL OF APPROXIMATE REASONING; v. 105, p. 287-304, FEB 2019.
• Citações Web of Science: 0

Resumo

Markov Decision Processes (MDPS) are commonly used to solve sequential decision problems. A less restrictive model is the Bounded-parameter MDP (BMDP) that allows: (i) the transition function to be expressed in terms of probability intervals and (ii) reasoning about a robust solution, i.e., the best solution under the worst model. In this paper, we propose the Robust Topological Policy Iteration (RTPI) algorithm which is a new policy iteration algorithm for infinite horizon BMDPs based on a partition of the state space. The empirical results show that the more structured the domain, the better is the performance of RTPI. (C) 2018 Elsevier Inc. All rights reserved. (AU)

• Processo FAPESP: 15/01587-0 - Armazenagem, modelagem e análise de sistemas dinâmicos para aplicações em e-Science
• Beneficiário: João Eduardo Ferreira
• Modalidade de apoio: Auxílio à Pesquisa - Programa eScience e Data Science - Temático