creighton-optimisingdiscrete-2002.pdf (286.47 kB)
Optimising discrete event simulation models using a reinforcement learning agent
conference contribution
posted on 2002-01-01, 00:00 authored by Douglas CreightonDouglas Creighton, Saeid NahavandiA 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.
History
Event
Winter Simulation. Conference (34th : 2002 : San Diego, California)Pagination
1945 - 1950Publisher
IEEE XploreLocation
San Diego, CaliforniaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2002-12-08End date
2002-12-11ISBN-13
9780780376144ISBN-10
0780376145Language
engNotes
©2002 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.Publication classification
E1.1 Full written paper - refereedCopyright notice
2002, IEEEEditor/Contributor(s)
E Yucesan, C Chen, J Snowdon, J CharnesTitle of proceedings
WSC 2002 : Exploring new frontiers : Proceedings of the 34th Conference on Winter SimulationUsage metrics
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