# UAV Cluster-Assisted Task Offloading for Emergent Disaster Scenarios
## 5 Performance Evaluation and Analysis
### Result Analysis
(**a**) Reward value versus number of iterations for the DRL-UCTO algorithm. (**b**) Loss versus training steps for different algorithms. increases, the UAV flight trajectory is optimized, the distance between the UAV cluster and the ground user is shrinking, and the energy consumed for transmission is decreasing. All three algorithms showed some improvement in the energy consumption generated by the system processing tasks, especially the DRL-UCTO algorithm, which kept the energy consumption of the system at a low level after 200 training episodes. In resource-limited disaster areas, the battery capacity limitation of the UAV will be a crucial factor in extending the life cycle of the system, so the energy efficiency of the UAV is an important indicator to verify the effectiveness of the algorithm. In this paper, the three algorithms are trained for 350 episodes under the same environmental parameters, and the energy efficiency of the UAVs changes as shown in Figure 5b. As the UAV moves, the distance between it and the ground user keeps growing closer, the energy consumption generated in data transmission keeps decreasing, and the energy efficiency is improved accordingly. Thus, the energy efficiency of the UAVs under the three algorithms continues to improve as the number of training episodes increases. As can be seen from the figure, the energy efficiency of the UAV with the DRL-UCTO algorithm is always higher than the other two baseline algorithms under the same training episodes, and has a stable trend after 300 training episodes, while the optimization of the energy efficiency of the UAV with the Greedy algorithm is less satisfactory and remains at a lower level. Figure 6 compares the behavioral policies obtained by the DRL-UCTO algorithm with other baseline algorithms for different numbers of ground user scenarios. As shown in the figure, DRL-UCTO shows better performance than other baseline algorithms for different numbers of user scenarios. With the increase in the number of users, the average throughput of the system shows an upward trend. However, the number of users becomes larger, making it impossible for the UAV to approach every user simultaneously, so the throughput growth is relatively slow. However, the DRL-UCTO algorithm still obtained higher throughput, and the DQN and Greedy algorithms only obtained suboptimal behavioral policies for the same number of training episodes, which could not be well adapted to different numbers of user scenarios, proving that the DRL-UCTO algorithm is more robust. As for the average energy consumption of the system, the DRL-UCTO algorithm shows a performance closer to the optimal global solution. As the number of ground users increases, the state space to be considered by the three algorithms further increases, and the difficulty of making behavioral decisions increases accordingly, leading to a rise in the average energy consumption of the system. However, the DRL-UCTO algorithm shows a slower growth trend than the other baseline algorithms, while the DQN and Greedy algorithms show comparable performance results. Because both the DQN and Greedy algorithms are suboptimal policies leading to significant variations in the results. Figure 5: Impact of different algorithms on system energy indicators. (**a**) System energy consumption versus training episodes. (**b**) System energy efficiency versus training episodes. In addition, we compare the DRL-UCTO algorithm with the DQN algorithm and the Q-learning algorithm described in [38] under a scenario with 30 users to explore the effect of deep reinforcement learning versus traditional reinforcement learning on the average energy consumption of the system. As shown in Table 3, \(S\) and \(A\) denote the state and action space size, respectively, and \(T\) denotes the number of time slices for each training episode.