## 5 Results and Discussion
### Simulation Results  
To demonstrate the efficiency of the proposed MHPPO method, we compared its performance with the benchmark methods, as detailed below:  
(1) HPPO: The method does not employ an action masking mechanism, allowing the agent to select from the full action space, which may result in the choice of invalid actions. (2) MDDPG: Based on the DDPG method, an action masking mechanism is the same to the proposed method is incorporated to filter out invalid actions. (3) DDPG: A traditional DDPG method is trained to learn the offloading and allocation policy. (4) RAMDOM: Task vehicles randomly select actions. (5) LOC: All tasks are computed locally. The convergence performance of the proposed method under diverse learning rate combinations is illustrated in Figure 3. The results indicate that the proposed method achieves convergence within 150 iterations across most learning rate settings. And different learning rates exhibit varying effects on the convergence performance. Too large learning rates may lead to oscillations in the convergence curve, while too small learning rates may result in slower convergence speeds. Therefore, we set the learning rate to 0.001 for the actor network and 0.01 for the critic network in the simulation. To validate the effectiveness and superiority of the proposed method, Figure 4 illustrates the curves of the average AoI versus the number of iterations for the proposed MHPPO and the other benchmark methods. The results indicate that the proposed method  
Figure 3: Rewards for different learning rates. consistently outperforms the baseline methods, and compared with the LOC, it achieves about a 113% reduction in average AoI. These improvements are attributed to the edge-assisted computing scenario considered in this work, which makes full use of more computational resources for optimization, and at the same time introduces the action masking mechanism to effectively shield illegitimate actions. To investigate how the quantity of task vehicles influences the average AoI, Figure 5 illustrates the average AoI performance of the six methods with varying numbers of task vehicles. The number of task vehicles ranges from 10 to 50, increasing by 10 each time. The results indicate that, apart from LOC, the average AoI of the other five methods rises as the number of task vehicles increases. This can be attributed to the fact that, under conditions of constrained edge computing and communication resources, an increase in the number of users leads to a higher task load, while the communication resources allocated to each task vehicles decrease, thereby leading to a rise in task latency. Specifically, the proposed MHPPO method demonstrates a noticeable increase in AoI, but it remains the best in terms of performance among all these six methods. And the performance degradation is more gradual compared to other baseline methods, indicating its robustness in handling a higher number of task vehicles. In contrast, the average AoI of the LOC is basically unaffected by the change in the number of task vehicles because it only relies on the computational capability of the local device itself, which is independent of the number of task vehicles. In conclusion, as the number of task vehicles grows, the proposed MHPPO consistently demonstrates better AoI performance compared to the other baseline methods. To explore how task data volume influences the average AoI, Figure 6 compares the AoI performance of different methods under varying data size ranges. The experiment systematically evaluates the effect of task data volume on AoI by adjusting the data size from [40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50] Kbit to [80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90] Kbit. The number of task vehicles is 20. The results demonstrate that the average AoI gradually increases as the data size increases.