# Vibration Control with Reinforcement Learning Based on Multi-Reward Lightweight Networks
## 4 Experimental Results and Discussion
### Demonstrating the Feasibility of This Lightweight Method  
To verify the feasibility of the lightweight method, we compared the performance of the fully connected networks under different settings. In this experiment, the Actor network structure was [400, x, x, 4], and the Critic network structure was [400, x, x, 1], where x is an integer. For both Figure 10(a),b, we gradually increased the size of the neural network's intermediate layers from 8 to 16, to 32, to 64, to 128, doubling each layer. In Figure 10(a), the different colors represent different numbers of intermediate layers in the Critic network. As the number of intermediate layers in the Actor network increased, the effectiveness of vibration control gradually improved. In Figure 10(b), the different colors represent different numbers of intermediate layers in the Actor network. As the number of intermediate layers in the Critic network increased, the effectiveness of vibration control also increased. In our DRSL-MPER method, the Actor and Critic networks used the same network structure, with parameter counts of 22,584 and 44,970, respectively, achieving a control effect of 20.24 dB. In contrast, when the intermediate layer size was 64, the Actor had 30,084 parameters, the Critic had 59,906 parameters, and the control effect reached 5.358 dB, which is close to our method in terms of the parameter count. This indicates that DRSL-MPER increased the damping effect by 14.882 dB with fewer parameters. In comparison, the best control effect occurred when the intermediate layers of both the Actor and Critic were 256, achieving a control effect of 15.57 dB. The Actor had 169,476 parameters, the Critic had 337,922 parameters, and DRSL-MPER improved the control effect by 4.67 dB with only 13% of the parameters of the 256-layer network. These results clearly demonstrate the significant advantages of our lightweight network in relation to both its effectiveness and its parameter count.  
\begin{table}
\begin{tabular}{c c} \hline
**V, MSV, RMS, OM** & **Reduction (dB)** \\ \hline RMS, OM & 9.238 \\ V, MSV, RMS & 16.402 \\ V, MSV, OM & 15.598 \\ V, RMS, OM & 2.234 \\ MSV, RMS, OM & 15.074 \\ V, MSV, RMS, OM & 13.858 \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 11: Impact diagrams of different Actor/Critic parameters on vibration control. Through experiments with varying numbers of FC layers in the Actor/Critic networks, this study demonstratesthe superiority of the proposed method in terms of both the parameter efficiency and control effectiveness. (**a**) With fixed Critic parameters, the effect of Actor parameter variation on vibration control. Here, the Actor network structure is [400, x, x, 4], where the horizontal x-axis represents a reduction in Actor network layers, and the vertical axis denotes the control rate (percentage), the ratio of controlled to uncontrolled states before and after the control process. (**b**) With fixed Actor parameters, the effect of Critic parameter variation on vibration control. The Critic network structure is [400, x, x, 1], where the horizontal x-axis represents a reduction in Critic network layers, and the vertical axis denotes the control rate (percentage), the ratio of controlled to uncontrolled states before and after the control process.