## 5 Experiments
Moreover, the arrow lengths convey the action magnitudes, with longer arrows denoting larger actions and suggesting the model's heightened assurance in its responses to those states. Conversely, shorter arrows could denote a more tentative or measured approach by the model in certain states. It is particularly interesting to observe that within the core region of the feature space, both the direction and magnitude of the arrows maintain a remarkable consistency. This pattern implies that the model potentially adopts a more stable or decisive strategy when \begin{table}
\begin{tabular}{l l l} \hline \hline
**Hyperparameter** & **Value** & **Description** \\ \hline Learning Rate (\(\alpha\)) & \(1\times 10^{-3}\) & Learning rate for policy updates. \\ \hline Meta-Learning Rate (\(\beta\)) & 0.001 & Learning rate for meta updates in Reptile. \\ \hline Training Epochs (T) & 2500 & Total number of training epochs. \\ \hline Task Sample Number (N) & 5, 10, 15, 20 & Number of tasks sampled per meta-update. \\ \hline Gradient Update Steps (K) & 5 & Number of gradient update steps per task. \\ \hline GAIL Discriminator Architecture & 2 layers, 64 neurons & Two-layer network for the GAIL discriminator, each layer with 64 neurons. \\ \hline GAIL Discriminator Learning Rate & \(3\times 10^{-4}\) & Learning rate for the GAIL discriminator. \\ \hline Expert Demonstration Dataset Size & 1000 pairs & Size of the expert demonstration dataset (state–action pairs). \\ \hline Policy Network Architecture & 2 layers, 64 neurons & Two-layer network for the policy, each layer with 64 neurons. \\ \hline Policy Network Learning Rate & \(3\times 10^{-4}\) & Learning rate for the policy network. \\ \hline Reward Scaling Factor & 0.01 & Scaling factor for rewards in GAIL. \\ \hline Exploration Strategy & Epsilon-greedy & Epsilon-greedy strategy with initial epsilon of 1, decaying to 0.1. \\ \hline Batch Size & 64 & Batch size for training updates. \\ \hline Regularization Parameter & \(1\times 10^{-3}\) & L2 regularization coefficient. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hyperparameter configuration for the experiment. Figure 8: Drone three-dimensional space control task simulation diagram. encountering states within this area. Additionally, in the top-right quadrant of the diagram, we notice a sequence of elongated arrows all pointing in the same direction, suggesting that the model exhibits a pronounced and robust inclination towards certain actions in these specific zones. In this experimental environment, we conducted comparative tests of our algorithm using the PPO algorithm as a baseline. As shown in Figures 11 and 12, the use of either GAIL (Generative Adversarial Imitation Learning) alone or Reptile alone positively impacted the results, with GAIL showing particularly noticeable effects. Our combined algorithm, Reptile+GAIL, demonstrated even more superior performance. Especially in the early stages of training, our algorithm was able to achieve good results quickly. During the convergence phase, compared to using GAIL alone, our algorithm exhibited better stability (Table 3). This success is attributed to the rapid adaptability to new tasks of meta-learning and the guiding role of expert data in imitation learning. Figure 10: In cases of poor training results, we observe that the distribution of the arrows is more scattered, indicating that a unified and stable strategy has not yet been successfully developed. Figure 9: The visualization strategy displayed when the training results are satisfactory. Figure 11: Comparative analysis of algorithm performance. Figure 12: This graph illustrates the effect of varying the number of task samples. It can be observed that with the increase in the number of tasks, the performance of the algorithm correspondingly improves. Additionally, we conducted a thorough analysis of the process for generating expert trajectories and observed some key findings.