Optimizing of support plan for new graduate employment market : Reinforcement learning

We focused on the problems of the new graduate market in Japan, where the recruitment period starts simultaneously. Therefore the competition among students become fierce, and many students spend a lot of time and efforts for their recruitment activity, but their behaviors are not effective. In order to clarify these problems, we conducted the multi-agented simulation with reinforcement learning. After dividing students into six groups by their ability and aggressiveness, we executed two types of support plans by Actor Critic which is the one of reinforcement learning. Then it was found that the support plans which encourage the students, whose abilities are middle-level and aggressiveness are low-level, are effective to increase final finding employment rate in the recruitment market.