Analysis of Q-Network structure in deep Q learning to optimize elevator control
深層 Q 学習によるエレベータ制御最適化のための Q-Network 構造の検討

Wataru YOSHIDA, Naoki MORI
In recent years, deep reinforcement learning has made remarkable progress. In this study, I focused on an elevator control problem, one of the important reinforcement learning tasks. In this problem, in a building with multiple elevators, it is necessary to optimize the behavior of the car so that the waiting time for passengers is reduced. Therefore, approaches using deep reinforcement learning for optimization of the controller have recently attracted attention. In this study, I aimed to
more » ... ve passenger's transportation efficiency on a time average by modifying the controller in the previous study, which is implemented using Deep Q-Network (DQN), one of the deep reinforcement learning. So I propose two Q-Network structures, which have the role of determining Q score in DQN. From the experimental results, it is confirmed that both of the proposed Q-Network structures function more effectively than the conventional method in a specific time zone.
doi:10.11517/pjsai.jsai2020.0_4g2gs701 fatcat:nbzspldaebe2hjfpmtmn6se7bq