## 5 Experimental Analysis
### Comparison of System Metrics  
In the in situ data management process of the smart grid InS system, this study uses three system evaluation metrics: AWD, ARR, and ARL, to assess the management strategies driven by different algorithms. To observe the performance of the strategies under different data access demand generation rates, two sets of stress tests are set with \(\zeta=1.0\) and \(\zeta=2\).  
Figure 5 illustrates the performance trends of each algorithm during the experimental process under a system load level of \(\zeta=1.0\). Although the static management strategies  
Figure 4: Convergence performance. This figure illustrates the reward convergence process of three reinforcement learning comparison algorithms within a limited number of episodes. It also compares the reward convergence trends of each algorithm under different system load conditions (\(\zeta=1.0\) and \(\zeta=1.5\)).  
driven by Baseline and ABC slightly mitigate the growth of system delay and congestion, all three performance metrics continue to rise overall, indicating a lack of effective response and scheduling optimization in complex dynamic environments. In contrast, the three reinforcement learning algorithms are able to learn more optimal scheduling strategies through continuous interaction with the environment. Within the predefined training rounds, all performance indicators exhibit clear convergence trends. Among them, the DDQN-PER algorithm, by introducing a prioritized experience replay mechanism, enhances the learning of critical experiences while improving sampling efficiency, resulting in more efficient policy updates during training. This algorithm outperforms others across all three optimization metrics and demonstrates significant advantages in convergence speed and stability, showcasing stronger generalization capabilities and system adaptability.  
Figure 6 illustrates the performance trends of each algorithm during the experiment when the system load increases to \(\zeta=1.5\). Compared to the results under \(\zeta=1.0\), it is evident that the static management strategies driven by Baseline and ABC almost completely fail under high-load conditions. All three performance indicators continuously rise without exhibiting any convergence trend, indicating a lack of dynamic adaptation and global optimization capability under increased system pressure. In contrast, although the indicator levels of the three reinforcement learning algorithms increase under high load compared to \(\zeta=1.0\), they still exhibit convergence trends and remain stable within a relatively small range. These algorithms are capable of continuously optimizing policies and mitigating performance degradation by capturing system state changes. Among them, DDQN-PER demonstrates the most robust adaptability and generalization capability, benefiting from its prioritized experience replay mechanism, which significantly improves sampling efficiency and the utilization of critical experiences. As a result, it surpasses other algorithms in convergence speed, performance stability, and final metric performance.  
A comparison of algorithm performance under different system load levels is provided in Table 4.  
Figure 5: The system performance comparison when \(\zeta=1.0\). The trends of all three performance indicators under different algorithms within a limited number of training episodes are illustrated. The first row presents the indicator variations for the two traditional algorithms, while the second row shows the results for the three reinforcement learning algorithms. The colored shaded areas represent the fluctuation range across ten independent experiments. Polynomial fitting is applied to these areas to highlight the overall trend of the indicators.