## 5 Results and Discussion
### Simulation Results
Moreover, among all benchmark methods, the proposed MHPPO method achieves better performance, maintaining the lowest average AoI across all tested data sizes, and its AoI tends to change more gently with the increase of task data volume, which further validates its superiority. Figure 4: AoI for different methods. To investigate how the quantity of service vehicles affects performance, Figure 7 shows the average AoI performance for both methods across varying numbers of service vehicles. The system configuration consists of 20 task vehicles with data sizes ranging from 100 to 110 Kbit. When increasing the number of service vehicles from 0 to 20 in increments of 5, experimental results demonstrate a decrease in average AoI. Notably, the scenario without any service vehicles exhibits about a 45.3% performance gap compared to the scenario equipped with 20 service vehicles. This is due to the fact that more computing resources are available to process the tasks more efficiently, thus reducing the information delay. Meanwhile, the proposed method has better performance. To investigate the impact of two-way traffic on computation offloading performance and stability of the system, Figure 8 shows the rewards performance for both the one-way traffic scenario and two-way traffic scenario. The scenarios include two experimental settings with task vehicle counts of 10 (TV = 10) and 20 (TV = 20), respectively. The results show that there is no significant difference in system performance between them. This can be attributed to the fact that offloading efficiency is primarily constrained by computational and communication resources rather than vehicle movement patterns. The reward values converge to similar levels, indicating that the traffic direction has minimal impact on computational offloading efficiency. And system stability remains unaffected, as curves remain stable in both one-way and two-way conditions without significant fluctuations or performance degradation. Figure 5: Average AoI with the varying number of task vehicles. Figure 6: Average AoI with different data size of task. To investigate the impact of service nodes' scheduling strategies on system performance, Figure 9 compares the reward function curves under three distinct task scheduling policies:  
(1) FCFS: The scheduling strategy adopted by the service nodes in this paper. (2) Shortest-Job-First (SJF): Task with the smallest data volume is processed first. (3) Priority: Tasks are processed according to their predefined priority levels. Figure 8: Rewards under different traffic directions. Figure 7: Average AoI with varying number of service vehicles. Figure 9: Rewards under different scheduling strategies. The results show that the FCFS scheduling policy achieves relatively better performance compared to the priority and SJF strategies. Specifically, the FCFS strategy stabilizes faster and maintains a higher reward compared to the other two policies. And the SJF policy has the lowest rewards; a potential explanation is that the system prioritizes scheduling smaller tasks, resulting in prolonged accumulation of larger tasks and consequently increasing the overall waiting time. In conclusion, the proposed MHHPO method maintains stable performance regardless of the FCFS, SJF, and priority scheduling policies, indicating its strong adaptability to diverse scheduling policies across service nodes. To investigate the impact of task arrival models on system performance, Figure 10 compares the reward function curves under three distinct arrival models: task arrival model based a Bernoulli process, task arrival model based on Poisson, and Pareto-distributed bursty traffic model. It is observed that all task arrival models eventually converge to the same reward value while the number of task vehicles is 10 (TV = 10). This suggests that when the system is lightly loaded, the arrival model has negligible impact on overall performance, as the system can handle tasks efficiently regardless of their distribution.