# MADDPG-Based Offloading Strategy for Timing-Dependent Tasks in Edge Computing
## 5 Experimental Results and Discussion
### Parameter Settings
Figure 5 compares the DQN_DTP_TO algorithm and the DDQN_DTP_TO algorithm with the sequential offloading based on DQN and DDQN, respectively, and the training results show that as the learning rounds continue to increase, the response latency of the offloading algorithms in combination with the DTP algorithms is lower than that of the baseline algorithm, and the offloading latency is improved by nearly 6.9% in terms of reducing the offloading latency. The proposed DTP algorithm is suitable for deep reinforcement-learning offloading algorithms, maintains stability in DRL algorithm training, and improves better adaptation in terms of task-offloading efficiency. Therefore, in MEC network scenarios, the rational use of hierarchical dependencies between different tasks and the resources of multiple edge nodes can effectively improve the response rates of end-users, and the proposed DTP algorithm lays the foundation for the subsequent processing and offloading tasks in more complex application scenarios. Figure 6 compares the convergence performance of the proposed UBP_DTO with the other DRL offloading algorithms. As shown in Figure 6, under the same conditions, the proposed UBP_DTO algorithm converges after nearly 500 Episode rounds, and not only converges faster but also the reward value is significantly better than the other three compared algorithms. This is due to the fact that the proposed algorithm utilizes the GRU \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Location and service radius of edge nodes & (1,2,0) (2,1,0) (2,2,0.1) 1.5 km \\ Location and sensing radius of the agent & (1,1,0) (3,1,0) (2,3,0) 1.5 km \\ Terminal CPU frequency & \(2\times 10^{10}\) cycles/s \\ CPU frequency of the edge node & \(8\times 10^{11}\) cycles/s \\ Task computing resource allocation ratio & \(0.27\times\) task size bit \\ Total computing resources of the terminal & 10 MB \\ Total computing resources of the edge nodes & 35 MB \\ Uplink/downlink transmission power of edge nodes & 35 W \\ Transmission bandwidth of edge nodes & 10 MHz \\ Task data size & [640, 6400] KB \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. module to record the feature attributes of the timing-dependent tasks and the historical offloading decisions to help the agent predict the attribute information of the next upcoming task. It reduces the complexity of the scenarios in the successive decision-making environment on the basis of the MADDPG offloading algorithm and thus improves the offloading decision-making efficiency. The combination of MADDPG and GRU enables the agent not only to make the optimal decision based on the current state but also to better predict the future state based on the historical behavior and the trend of the environment. Meanwhile, the GRU module can help the algorithm better adapt to the dynamically unstable environment when it needs to consider other agent strategies and environment states. Therefore, compared with the single-agent algorithms, the proposed algorithm obtains higher rewards in the early stage of learning. It effectively illustrates the effectiveness of the offloading decisions made by the MADDPG-based multi-agent offloading algorithm compared to the single-agent offloading algorithm even in the early stage of training. In addition, as shown in Figure 7 about the convergence details of the two algorithms MADDPG_DTO and UBP_DTO, compared with MADDPG_DTO algorithm, UBP_DTO algorithm short 100 episodes and have a higher reward convergence value. Therefore, the GRU-MADDPG-based task offloading scheme effectively improves the training rate and training effect of MADDPG. Figure 5: Fitness performance of two DRL algorithms combined with the DTP algorithm. Figure 6: Comparison of the delay of the DTP algorithm combined with two DRL algorithms: (**a**) DQN and (**b**) DDQN. Figure 8 demonstrates the performance comparison of the proposed algorithm with five other benchmark algorithms in terms of offloading delay.