Approximation benefits of policy gradient methods with aggregated states
Daniel Russo
| Year of publication: |
2023
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|---|---|
| Authors: | Russo, Daniel |
| Published in: |
Management science : journal of the Institute for Operations Research and the Management Sciences. - Hanover, Md. : INFORMS, ISSN 1526-5501, ZDB-ID 2023019-9. - Vol. 69.2023, 11, p. 6898-6911
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| Subject: | approximate dynamic programming | policy gradient methods | reinforcement learning | state aggregation | Theorie | Theory | Mathematische Optimierung | Mathematical programming | Dynamische Optimierung | Dynamic programming |
Online Resource
| Type of publication: | Article |
|---|---|
| Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
| Language: | English |
| Other identifiers: | 10.1287/mnsc.2023.4788 [DOI] |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10014435438