Learning-agent-based simulation for queue network systems

Daniel Barry Fuller, Edilson Fernandes de Arruda and Virgílio José Martins Ferreira Filho

Journal of the Operational Research Society, 2020, vol. 71, issue 11, 1723-1739

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.

Date: 2020
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DOI: 10.1080/01605682.2019.1633232

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