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Optimising of support plans for new graduate employment market using reinforcement learning
by Keiko Mori, Setsuya Kurahashi
International Journal of Computer Applications in Technology (IJCAT), Vol. 40, No. 4, 2011

 

Abstract: We focus on the job matching processes in the market for new graduates in Japan, where students and companies select each other. This job matching process does not always function effectively. We conducted an agent-based simulation with reinforcement learning in order to confirm the phenomenon in the market. We adopted two types of reinforcement learning: the Profit Sharing method and the Actor-Critic method. After some experiments, it was found that both methods effectively support students' job-hunting activities and raise the finding-employment proportion of the entire graduate employment market.

 

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