| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [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
Total Affiliations: 2
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| Document type: | Journal article |
| Source: | INTERNATIONAL JOURNAL OF APPROXIMATE REASONING; v. 105, p. 287-304, FEB 2019. |
| Web of Science Citations: | 0 |
| Abstract | |
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) | |
| FAPESP's process: | 15/01587-0 - Storage, modeling and analysis of dynamical systems for e-Science applications |
| Grantee: | João Eduardo Ferreira |
| Support Opportunities: | Research Grants - eScience and Data Science Program - Thematic Grants |