Gradient-based Learning in State-based Potential Games for Self-Learning Production Systems

  • In this paper, we introduce novel gradient-based optimization methods for state-based potential games (SbPGs) within self-learning distributed production systems. SbPGs are recognised for their efficacy in enabling self-optimizing distributed multi-agent systems and offer a proven convergence guarantee, which facilitates collaborative player efforts towards global objectives. Our study strives to replace conventional ad-hoc random exploration-based learning in SbPGs with contemporary gradient-based approaches, which aim for faster convergence and smoother exploration dynamics, thereby shortening training duration while upholding the efficacy of SbPGs. Moreover, we propose three distinct variants for estimating the objective function of gradient-based learning, each developed to suit the unique characteristics of the systems under consideration. To validate our methodology, we apply it to a laboratory testbed, namely Bulk Good Laboratory Plant, which represents a smart and flexibleIn this paper, we introduce novel gradient-based optimization methods for state-based potential games (SbPGs) within self-learning distributed production systems. SbPGs are recognised for their efficacy in enabling self-optimizing distributed multi-agent systems and offer a proven convergence guarantee, which facilitates collaborative player efforts towards global objectives. Our study strives to replace conventional ad-hoc random exploration-based learning in SbPGs with contemporary gradient-based approaches, which aim for faster convergence and smoother exploration dynamics, thereby shortening training duration while upholding the efficacy of SbPGs. Moreover, we propose three distinct variants for estimating the objective function of gradient-based learning, each developed to suit the unique characteristics of the systems under consideration. To validate our methodology, we apply it to a laboratory testbed, namely Bulk Good Laboratory Plant, which represents a smart and flexible distributed multi-agent production system. The incorporation of gradient-based learning in SbPGs reduces training times and achieves more optimal policies than its baseline.show moreshow less

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Author:Steve YuwonoORCiD, Marlon Löppenberg, Dorothea Schwung, Andreas SchwungORCiD
open access:Green - Zweitveröffentlichung – meist in Form eines Manuskripts (z. B. Preprint oder Postprint)
Fachbereich/Einrichtung:Hochschule Düsseldorf / Fachbereich - Elektro- & Informationstechnik
Document Type:Preprint
Year of Completion:2024
Language of Publication:English
Publisher:arxiv
Parent Title (English):arxiv
DOI:https://doi.org/10.48550/arXiv.2406.10015
ArXiv Id:http://arxiv.org/abs/2406.10015
Tag:Artificial Intelligence; Computer Science and Game Theory; Machine Learning
GND Keyword:Maschinelles Lernen; Künstliche Intelligenz; Spieltheorie
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften / 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
Licence (German):Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International
Release Date:2024/12/10
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