Optimising discrete event simulation models using a reinforcement learning agent
Contribuinte(s) |
Yucesan, E. Chen, C.-H. Snowdon, J.L. Charnes, J.M. |
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Data(s) |
01/01/2002
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Resumo |
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.<br /> |
Identificador | |
Idioma(s) |
eng |
Publicador |
IEEE Xplore |
Relação |
http://dro.deakin.edu.au/eserv/DU:30022616/creighton-optimisingdiscrete-2002.pdf http://dx.doi.org/10.1109/WSC.2002.1166494 |
Direitos |
2002, IEEE |
Tipo |
Conference Paper |