# Optimized Dynamic Deployment of UAVs in Maritime Networks with Route Prediction
## 5 Simulation Results and Discussion
### Performance Evaluation of UAV Dynamic Deployment Algorithms  
In this section, we evaluate the performance of the proposed RUDD algorithm through simulation tests. In the simulation experiments, we compare the proposed RUDD algorithm, PPO alone, Deep Deterministic Policy Gradient (DDPG) algorithm, and Random policy (Random) algorithm for the network coverage, the latency of the user to receive the service, and the average energy consumption of the UAVs for completing the service. The main simulation parameters of the RUDD algorithm are shown in Table 1.  
In this experiment, the parameters we chose include the number of mobile devices, the number of UAVs, the discount factor \(\gamma\), the decay factor \(\lambda\), the number of training rounds, the Critic learning rate, and the Actor learning rate in \(A^{\pi_{\text{old}}}\). The number of mobile devices will determine their initial distribution and location, which determines the complexity of the task and the system load, and setting it to \([10,15,20,25,30]\) can test the adaptability and robustness of the algorithm under different densities of user scenarios. The number of drones is fixed at 3, which determines the service capacity and coverage, thus placing restrictions on the computational capacity, energy consumption, and coverage of the whole algorithm. The discount factor \(\gamma\) can determine how much importance the  
Figure 2: (**a**) Comparison of trajectories predicted by each algorithm; (**b**) MLP algorithm trajectory-prediction graph; (**c**) LSTM algorithm trajectory-prediction graph; (**d**) SSA-LSTM algorithm trajectory-prediction graph. algorithm attaches to future rewards, while the decay factor \(\lambda\) can be used to control the balance between the variance and bias of the dominance estimation, and setting them to 0.95 and 0.9, respectively, can make the algorithm pay more attention to long-term rewards. The algorithm tends to converge at around 200 training rounds and thus is set to 200. The Critic network learning rate can be used to update the value function quickly, and the Actor-network learning rate can be used to avoid the model instability caused by a too fast strategy update. Setting them to 0.02 and 0.01, respectively, can help the smooth optimization of the strategy, which can improve the robustness of the algorithm. As shown in the comparison of the total reward curves of the four algorithms in Figure 4, the PPO algorithm performed more stably and had relatively higher rewards than the Random and DDPG algorithms. The PPO used a restrained update mechanism, which ensured smoothness during training by limiting the magnitude of each policy update, thus avoiding excessive policy fluctuations. The Random strategy, due to the lack of a learning mechanism, did not perform significantly better as the number of training times increased and thus performed relatively poorly. In this experimental setting, the DDPG's reliance on empirical playback and unconstrained policy updates results in its vulnerability to noise and outdated data during training. As a result, the DDPG algorithm fluctuates and struggles to converge in reward values as the number of training sessions increases. Compared to these three, RUDD adds a prediction algorithm on top of PPO, which enables the UAV to predict the distribution of users in advance, thus optimizing policy updates and making appropriate action choices. As a result, the UAV can formulate the best deployment method before the task execution, which not only ensures coverage but also effectively reduces flight delay and energy consumption. This makes RUDD show strong adaptability and stability during the training process, and thus the reward value after final convergence is the largest. The RUDD algorithm can predict environmental changes more accurately in complex and dynamic marine environments, thus realizing the dynamic deployment