## 5 Control Performance Evaluation of the DQN Model
However, the peak 11 th-story drift of the passive-off case is controlled better than in the other control cases, except for the DQN case. This is because the minimum MR damper force in the SMIS allows the upper isolated structure to move like a tuned mass damper (TMD). The isolated upper story structure's moving like an auxiliary mass results in the largest roof story responses, compared to all other control cases. Tables 3 and 4 show that the DQN model can reduce both the peak 11 th-story and isolation story drifts by about 30 %, compared to the original passive case. This shows that the DQN model is the most effective control algorithm among the five control cases. Tables 3 and 4 reveal that the reduction of the isolation story drift is in conflict with the decrease of the 11 th-story drift. That is, a reduction of the isolation story drift cannot be accomplished without an increase of the 11 th-story drift, whereas the 11 th-story drift can be decreased with an increment of the isolation story drift. Tables 3 and 4 show that the DQN control algorithm can effectively reduce both the peak 11 th story and isolation story drifts that are in conflict, by including them in the reward equation. Figure 17 shows the peak inter-story drifts of each control case along the story. The isolation story drifts of each model are excluded in this figure, because they are much larger than the other inter-story drifts, but they are quantitatively compared in Tables 3 and 4. The largest inter-story drift can be seen at the 11 th story from every control algorithm in Figure 17, because the 11 th story stiffness of the Shiodome Sumitomo building is the smallest. The DQN model provides good control performance not only for the 11 th-story drift, but also for all the story drifts, compared to the other control algorithms. The control effectiveness of the DQN model is much better than that of the comparative controllers, especially in the lower structure. Compared to the other control cases, both the DQN and skyhook controllers can effectively decrease the inter-story drifts in the upper structure. The action of the DQN agent is promptly determined based on the states, including the ground motion series and the dynamic responses of the sample structure. In this study, the action of the DQN agent is interpreted as the command voltage to control the MR damper. Figure 18 presents the command voltage time histories of the DQN and two comparative control algorithms for the artificial earthquake of EV1. It can be seen that the command voltage generated from the groundhook or skyhook algorithm is similar to the output of a simple on-off control algorithm, which provides 0 or 5 V. However, the command voltage generated from the DQN algorithm varies irregularly between 0 and 5 V with a step of 0.5 V. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Control** & **Peak S (m)** & \begin{tabular}{c} **Peak Isolation** \\ **Story Drift (m)** \\ \end{tabular} & \
\begin{tabular}{c} **Peak Roof** \\ **Displacement (m)** \\ \end{tabular} &
\begin{tabular}{c} **Peak Roof** \\ **Acceleration (m/s\({}^{2}\))** \\ \end{tabular} \\ \hline Passive & 0.0861 (1.00) & 0.6279 (1.00) & 0.7945 (1.00) & 11.2183 (1.00) \\ Groundhook & 0.0826 (0.96) & 0.5184 (0.83) & 0.7205 (0.91) & 9.7622 (0.87) \\ Skyhook & 0.0741 (0.86) & 0.5995 (0.95) & 0.6332 (0.80) & 8.5115 (0.76) \\ Passive-On & 0.0837 (0.97) & 0.4872 (0.78) & 0.7166 (0.90) & 9.404 (0.84) \\ Passive-Off & 0.0645 (0.75) & 0.8256 (1.31) & 0.7430 (0.94) & 12.3944 (1.10) \\ DQN & 0.0630 (0.73) & 0.4162 (0.66) & 0.5331 (0.67) & 8.5569 (0.76) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of peak responses according to control algorithms (EV2). When the isolation story drift is too big, the DQN agent increases the command voltage resulting in an increase of the MR damper force to reduce the isolation story drift and the movement of the isolated upper structure.