Approximations on risk-averse Markov decision processes
We consider the problem of approximating the values and the optimal policies in risk-averse discounted Markov Decision Processes with infinite horizon. We study the properties of the rolling horizon and the approximate rolling horizon procedures, proving bounds which imply the convergence of the pro...
| Autores: | , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2013 |
| País: | Argentina |
| Recursos: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/15583 |
| Acesso em linha: | http://hdl.handle.net/11336/15583 |
| Access Level: | acceso abierto |
| Palavra-chave: | MARKOV DECISION PROCESSES RISK AVERSION ROLLING HORIZON https://purl.org/becyt/ford/1.1 https://purl.org/becyt/ford/1 |
| Resumo: | We consider the problem of approximating the values and the optimal policies in risk-averse discounted Markov Decision Processes with infinite horizon. We study the properties of the rolling horizon and the approximate rolling horizon procedures, proving bounds which imply the convergence of the procedures when the horizon length tends to infinity. We also analyze the effects of uncertainties on the transition probabilities, the cost functions and the discount factors. |
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