## 6 Experiment Results
### Experimental Results
#### 6.2.1 Model Training and Comparative Experiments  
In the simulation environment, each experiment will randomly generate a total of 100,000 tasks, with a task arrival rate of 10,000 tasks per second. The simulation tasks used in this study are not derived from a public dataset but are synthetically generated. Key task parameters--such as image data size, model size, and computing power requirements--are generated using normal (Gaussian) distributions centered within their respective value ranges. This method ensures that most tasks fall within a typical operational range while still allowing some variation. The synthetic task dataset provides a controlled and flexible environment for evaluating task offloading strategies in IoV systems.  
The results are compared and analyzed using the following three scheduling strategies:  
* PER-iSAC (Proposed). The scheduling strategy using the PER-iSAC algorithm.
* Standard SAC. The scheduling strategy using the SAC algorithm.
* PPO Baseline. The scheduling strategy using the PPO algorithm [25].  
According to Figure 4, through the reward value curve in Figure 4a, it can be observed that PER-iSAC's total rewards rise rapidly in the initial phase (just as quickly as those of Standard SAC) and then level off. This indicates that both methods quickly improve performance during the early learning stage. Moreover, both algorithms converge faster, with smaller and more stable fluctuations. PPO's total rewards increase slowly at first and with greater fluctuations but begin to grow steadily after the 10 th episode, eventually reaching a relatively stable state. According to Figure 4b-d, the average queue rate, error rate, and completion time for all algorithms decrease rapidly in the initial phase. This suggests that the algorithms quickly refine their allocation strategy through the system's reward mechanism during the early learning stage. Overall, PER-iSAC is faster than Standard SAC and PPO Baseline in terms of reducing time and decreasing error rates. The main reason for this phenomenon is the introduction of the PER skill in PER-iSAC, which enhances iSAC's utilization of experiences that are of particularly high value but have a small sample size, thereby enabling PER-iSAC to explore actions that yield greater value earlier.  
According to Figure 5, all methods select servers in descending order of performance, with higher-performing servers being chosen more frequently, which aligns with the server performance settings in the predefined scenario. However, PPO Baseline has a significantly higher number of selections on Server 0 compared to the other algorithms, likely due to its better performance on that server and the acquisition of more rewards. This also implies that the PPO Baseline tends to select specific servers and does not distribute its selections as evenly across all servers as the other algorithms do. This could be due to PPO Baseline's preference for certain servers during the learning process or its insufficient learning of how to effectively utilize all servers. The reason for PPO Baseline's fluctuation could be that it has not achieved the optimal balance between exploring new strategies and exploiting known strategies. This could lead to it not fully utilizing all servers or not making the most optimal scheduling decisions in certain situations, resulting in instability during the training process.