Learning-agent-based simulation for queue network systems

Fuller, Daniel Barry, Fernandes De Arruda, Edilson and Ferreira Filho, Virgílio José Martins 2020. Learning-agent-based simulation for queue network systems. Journal of the Operational Research Society 71 (11) , pp. 1723-1739. 10.1080/01605682.2019.1633232

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Abstract

Established simulation methods generally require from the modeller a broad and detailed knowledge of the system under study. This paper proposes the application of Reinforcement Learning in an Agent-Based Simulation model to enable agents to define the necessary interaction rules. The model is applied to queue network systems, which are a proxy for broader applications, in order to be validated. Simulation tests compare results obtained from learning agents and results obtained from known good rules. The comparison shows that the learning model is able to learn efficient policies on the go, providing an interesting framework for simulation.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Mathematics
Publisher:	Palgrave Macmillan / Taylor & Francis
ISSN:	0160-5682
Date of First Compliant Deposit:	6 January 2020
Date of Acceptance:	28 May 2019
Last Modified:	24 Nov 2024 15:15
URI:	https://orca.cardiff.ac.uk/id/eprint/128221

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