General methodology 1: optimising discrete event simulation models using a reinforcement learning agent

Authors:
Douglas C. Creighton;Saeid Nahavandi
Affiliations:
Deakin University, Geelong, Victoria, Australia;Deakin University, Geelong, Victoria, Australia
Venue:
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Year:
2002

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Abstract

A reinforcement learning agent has been developed to determine optimal operating policies in a multi-part serial line. The agent interacts with a discrete event simulation model of a stochastic production facility. This study identifies issues important to the simulation developer who wishes to optimise a complex simulation or develop a robust operating policy. Critical parameters pertinent to 'tuning' an agent quickly and enabling it to rapidly learn the system were investigated.