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Optimising discrete event simulation models using a reinforcement learning agent

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2 Author(s)
Creighton, D.C. ; Sch. of Eng. & Technol., Deakin Univ., Geelong, Vic., Australia ; Nahavandi, S.

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.

Published in:

Simulation Conference, 2002. Proceedings of the Winter  (Volume:2 )

Date of Conference:

8-11 Dec. 2002

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