Estimation and control in finite Markov decision processes with the average reward criterion

• Autorzy: Rolando Cavazos-Cadena , Raúl Montes-de-Oca
• Języki publikacji: EN

Abstrakty

EN

This work concerns Markov decision chains with finite state and action sets. The transition law satisfies the simultaneous Doeblin condition but is unknown to the controller, and the problem of determining an optimal adaptive policy with respect to the average reward criterion is addressed. A subset of policies is identified so that, when the system evolves under a policy in that class, the frequency estimators of the transition law are consistent on an essential set of admissible state-action pairs, and the non-stationary value iteration scheme is used to select an optimal adaptive policy within that family.

Kategorie tematyczne

• 60J05: Discrete-time Markov processes on general state spaces
• 93E20: Optimal stochastic control
• 90C40: Markov and semi-Markov decision processes

• Wydawca: Institute of Mathematics Polish Academy of Sciences
• Czasopismo: Applicationes Mathematicae
• Rocznik: 2004
• Tom: 31
• Numer: 2
• Strony: 127-154

Daty

wydano

2004

Twórcy

autor

Rolando Cavazos-Cadena

• Departamento de Estadística y Cálculo, Universidad Autónoma Agraria Antonio Narro, Buenavista, Saltillo COAH 25315, México

autor

Raúl Montes-de-Oca

• Departamento de Matemáticas, Universidad Autónoma Metropolitana, Campus Iztapalapa, Avenida San Rafael Atlixco #186, Colonia Vicentina, México 09340, D.F., México

Identyfikatory

DOI

10.4064/am31-2-1