Efficiently breaking the curse of horizon in off-policy evaluation with double reinforcement learning
Nathan Kallus, Masatoshi Uehara
| Year of publication: |
2022
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|---|---|
| Authors: | Kallus, Nathan ; Uehara, Masatoshi |
| Published in: |
Operations research. - Linthicum, Md. : INFORMS, ISSN 1526-5463, ZDB-ID 2019440-7. - Vol. 70.2022, 6, p. 3282-3302
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| Subject: | infinite horizon | Machine Learning and Data Science | Markov decision processes | off-policy evaluation | semiparametric efficiency | Künstliche Intelligenz | Artificial intelligence | Theorie | Theory | Markov-Kette | Markov chain | Entscheidung | Decision | Lernprozess | Learning process | Data-Envelopment-Analyse | Data envelopment analysis | Nichtparametrisches Verfahren | Nonparametric statistics | Lernen | Learning |
Online Resource
| Type of publication: | Article |
|---|---|
| Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
| Language: | English |
| Other identifiers: | 10.1287/opre.2021.2249 [DOI] |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10014307802