## 6 Conclusions  
A reinforcement-learning-based semi-active control algorithm was developed in this study. The DQN was selected from among reinforcement learning methods for semi-active control, because it has shown many successful applications to various control engineering fields. The seismic response control performance of the DQN for the SMIS was investigated with comparative algorithms. For more practical study, the structural properties of the existing Shiodome Sumitomo building and the mid-story isolation system were directly used in this study. The sample SMIS is composed of MR dampers replacing nonlinear passive dampers in the existing building. The DQN reward was designed to reduce the isolation story and the peak inter-story (11 th story) drifts that are in conflict. The control performance of the DQN controller was compared with typical semi-active control algorithms, i.e., skyhook, groundhook, passive-on, and passive-off. Seismic responses of the original passive mid-story isolation system were used as the reference values to evaluate the control effectiveness of the SMIS controlled by five semi-active control cases. Two artificial ground motions were generated as seismic loads for training and evaluation of the DQN-based semi-active controller. Numerical analyses show that groundhook and skyhook controllers show good reduction performance of the isolation story and the 11 th-story drifts, respectively, in accordance with their design concept. Passive-on and passive-off control cases also effectively reduce the isolation story and the 11 th-story drifts, respectively, like groundhook and skyhook controllers. Because reduction of the isolation story drift is in conflict with decrease of the 11 th-story drift, these two structural responses cannot be easily simultaneously controlled by typical semi-active control policies. However, the DQN controller can effectively reduce both the 11 th story and isolation story drifts, compared to typical semi-active control algorithms. This means that the DQN network can successfully map the input state (seismic  
Figure 19: MR damper stroke-and-force relationship (EV1): (**a**) groundhook algorithm, (**b**) skyhook algorithm, (**c**) DQN algorithm. responses and ground motions) to the output action (control command), resulting in effective semi-active control. In this study, limited artificial ground motions were used for training and evaluation of the DQN controller. Accordingly, to increase the reliability of the semi-active control algorithm developed by the DQN method, other artificial or historical earthquake loads will be required for performance evaluation. Because the reward in the reinforcement learning method mainly affects the control performance of the DQN controller, future research will investigate the effects of various reward designs. Although the semi-active structural control system controlled by the DQN algorithm provides better control performance than the passive control system, the cost of the semi-active system is higher than that of the passive system. Whether the high cost of the semi-active system is reasonable depends on the project circumstances; thus, it is necessary to investigate the relationship between structural performance and economic feasibility in practical applications. Conceptualization, H.-S.K. and U.K.; methodology, U.K.; software, H.-S.K. ; validation, H.-S.K. and U.K.; writing--original draft preparation, H.-S.K. ; writing--review and editing, H.-S.K. and U.K.; visualization, U.K.; funding acquisition, H.-S.K. All authors have read and agreed to the published version of the manuscript. This research was supported by a National Research Foundation of Korea (NRF) grant, funded by the Korean government (MEST), grant number NRF-2019R1A2C1002385. Not applicable. The data presented in our study are available on request from the corresponding author. The authors express sincere gratitude to Department of Civil and Environmental Engineering of California State University, Fullerton for supporting sabbatical leave of H.-S.K.