Optimising discrete event simulation models using a reinforcement learning agent
Creighton, Douglas and Nahavandi, Saeid 2002, Optimising discrete event simulation models using a reinforcement learning agent, in WSC 2002 : Exploring new frontiers : Proceedings of the 34th Conference on Winter Simulation, IEEE Xplore, Piscataway, N.J., pp. 1945-1950.
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WSC 2002 : Exploring new frontiers : Proceedings of the 34th Conference on Winter Simulation
Yucesan, E. Chen, C.-H. Snowdon, J.L. Charnes, J.M.
Place of publication
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.
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