TY - JOUR
T1 - PARTIALLY OBSERVABLE MARKOV DECISION PROCESSES WITH PARTIALLY OBSERVABLE RANDOM DISCOUNT FACTORS
AU - Martinez-Garcia, E. Everardo
AU - Minjárez-Sosa, J. Adolfo
AU - Vega-Amaya, Oscar
N1 - Publisher Copyright:
© 2022 Institute of Information Theory and Automation of The Czech Academy of Sciences. All rights reserved.
PY - 2022
Y1 - 2022
N2 - This paper deals with a class of partially observable discounted Markov decision processes defined on Borel state and action spaces, under unbounded one-stage cost. The discount rate is a stochastic process evolving according to a difference equation, which is also assumed to be partially observable. Introducing a suitable control model and filtering processes, we prove the existence of optimal control policies. In addition, we illustrate our results in a class of GI/GI/1 queueing systems where we obtain explicitly the corresponding optimality equation and the filtering process.
AB - This paper deals with a class of partially observable discounted Markov decision processes defined on Borel state and action spaces, under unbounded one-stage cost. The discount rate is a stochastic process evolving according to a difference equation, which is also assumed to be partially observable. Introducing a suitable control model and filtering processes, we prove the existence of optimal control policies. In addition, we illustrate our results in a class of GI/GI/1 queueing systems where we obtain explicitly the corresponding optimality equation and the filtering process.
KW - discounted criterion
KW - optimal policies
KW - partially observable systems
KW - queueing models
KW - random discount factors
UR - http://www.scopus.com/inward/record.url?scp=85161883948&partnerID=8YFLogxK
U2 - 10.14736/kyb-2022-6-0960
DO - 10.14736/kyb-2022-6-0960
M3 - Artículo
AN - SCOPUS:85161883948
SN - 0023-5954
VL - 58
SP - 960
EP - 983
JO - Kybernetika
JF - Kybernetika
IS - 6
ER -