Using randomization to break the curse of dimensionality.
This paper introduces random versions of successive approximations and multigrid algorithms for computing approximate solutions to a class of finite and infinite horizon Markovian decision problems (MDPs). We prove that these algorithms succeed in breaking the “curse of dimensionality” for a subcla...
| Publicado en: | Econometrica Vol. 65; pp. 487 - 517 |
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| Formato: | Artículo |
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Wiley-Blackwell
May 1997
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=512991987&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 512991987 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00129682 ECN jtl: Econometrica issn: 00129682 maglogo: N pubinfo: dt: May 1997 vid: 65 pid: 480 pub: Wiley-Blackwell artinfo: ui: 512991987 10.2307/2171751 ppf: 487 ppct: 30 formats: tig: atl: Using randomization to break the curse of dimensionality. aug: au: Rust, John su: Markov processes Mathematical models of decision making Dynamic programming sug: subj: Markov processes Mathematical models of decision making Dynamic programming ab: This paper introduces random versions of successive approximations and multigrid algorithms for computing approximate solutions to a class of finite and infinite horizon Markovian decision problems (MDPs). We prove that these algorithms succeed in breaking the “curse of dimensionality” for a subclass of MDPs known as discrete decision processes (DDPs). Reprinted by permission of the Econometric Society. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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