Study on the application of single-agent and multi-agent reinforcement learning to dynamic scheduling in manufacturing environments with growing complexity: Case study on the synthesis of an industrial IoT Test Bed uri icon Wissenschaftlicher Artikel

Open Access Peer Reviewed Im Bibliotheksbestand

Überblick

Freigegeben

  • Ja

Freigegeben

  • Ja

Veröffentlichungszeitpunkt

  • 2024

Autor/in (verknüpft)

Open-Access-Kennzeichnung

  • Hybrid

Peer Reviewed

  • Ja

Sprachen

  • englisch

Im Bibliotheksbestand

  • Ja Im Bibliotheksbestand

Lizenz

  • CC BY V4.0 | Permits almost any use subject to providing credit and license notice. Frequently used for media assets and educational materials. The most common license for Open Access scientific publications. Not recommended for software. | http://creativecommons.org/licenses/by/4.0/

Forschung

Schlagwörter

  • Dynamic resource allocation
  • Industrial IoT Test Bed
  • Manufacturing Systems
  • Proximal policy optimization
  • Smart Production Systems
  • multi-agent reinforcement learning

Identität

International Standard Serial Number (ISSN )

  • 02786125

Alma MMS-ID

  • 99571128502581

weitere Informationen zum Dokument

Band

  • 77

Anderes

Kuratierungs-Zielkategorie (intern)

  • Ja