## 5 Experimental Evaluation
### Comparison Study and Discussion
#### 5.2.1 Task Scenario Description  
In the first experiment, to test the performance of the algorithm when the dependency situation between application subtasks changes, a test dataset with a different density than the training dataset was generated using the DAG task generator to simulate the offloading performance when a mobile device encounters this novel application. Subsequently, the performance of MTD3 CO and the baseline algorithm was compared for a small number of tasks offloaded on the test dataset. The parameters of this test dataset were as follows: number of subtasks \(N~{}=~{}20\), density = 0.4, and width = 0.8, where the density of DAG tasks did not appear in the training dataset, while both the number and the width of subtasks appeared. The energy consumption and latency for a small number of iterations are shown in Figure 5. HEFT could perform task offloading with a better strategy at the beginning because it predicts the partial offloading method and selects a better strategy, but it could not improve its strategy by increasing the number of iterations; hence, it remained at this performance, which was not the best. In this application, MTD3 CO outperformed all algorithms in terms of energy consumption and time delay after five iterations, indicating that it achieved meta-strategy adaptation for this application. In general, because it only considers one factor, the greedy algorithm had the highest latency and relatively high energy consumption. The MTD3 CO algorithm proposed in this paper outperformed the HEFT method in terms of latency and energy consumption after only a small amount of training, but the DRL method was still inferior to HEFT in both cases, and its energy consumption performance was less stable because the DRL method does not consider energy consumption. This shows that the MTD3 CO method is more flexible and adaptive to new tasks and applications. Regarding the constant values for the greedy and HEFT schemes, they are not dependent on the number of iterations, and their results are expected to remain constant. In other words, the energy consumption and latency values for greedy and HEFT are not supposed to change with the number of iterations.  
The second experiment aimed to test the performance of the algorithm when the subtask concurrency varies. The DAG width of the test dataset was different from the training dataset, and the width in this scenario was 0.9, simulating the extreme case of application offloading with high subtask concurrency. As shown in Figure 6, at this point, HEFT did not perform as well as DRL and MTD3 CO in terms of latency, and this fixed heuristic strategy tended to fall into suboptimal solutions when different applications were encountered. In terms of energy consumption performance, the DRL algorithm did not improve because it does not consider the energy consumption of the device and only takes the latency as the optimization objective. When faced with new applications, both MTD3 CO and DRL algorithms outperformed HEFT after a small number of updates, because both algorithms are methods for updating the strategy, which adjusts the strategy in real time using reinforcement learning's ability to adapt to the environment. They are more adaptable than algorithms with fixed policies like HEFT, and it can be seen that MTD3 CO could adapt to new tasks faster than the conventional DRL algorithm because of its use of internal and external loop learning and its ability to use prior learning experience to guide the learning of new tasks.  
Figure 5: Scenario 1 comparison of delay and energy consumption after a few iterations.  
Figure 6: Scenario 2 comparison of delay and energy consumption after a few iterations.