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http://hdl.handle.net/20.500.12984/7688
Título : | Partially observable Markov control models with random discount factors | Autor : | MARTÍNEZ GARCÍA, EDGAR EVERARDO MINJÁREZ SOSA, JESUS ADOLFO; 15176 |
Fecha de publicación : | jul-2021 | Editorial : | MARTÍNEZ GARCÍA, EDGAR EVERARDO | Resumen : | Three elements are needed to define an optimal control problem (OCP): (1) a decision or control model; (2) a set of admissible control policies, and (3) a performance index. So, the OCP is to find a control policy that minimizes the performance index. We can classify the OCPs in a variety of ways, for instance: deterministic or stochastic; continuous or discrete time; finite or infinite horizon; discounted or average cost performance index; with partial or complete state-system information, among others. In this work we focus on the study of OCPs considering partially observable discrete-time stochastic systems, under a discounted optimality criterion, but unlike the standard case, we assume random discount factors. | Descripción : | Tesis de maestría en ciencias matemáticas | URI : | http://hdl.handle.net/20.500.12984/7688 | ISBN : | 2210683 |
Aparece en las colecciones: | Maestría |
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