## 4 Results
### Comparison and Evaluation Results of Different Models  
We used the mean absolute error (MAE) to evaluate the similarity between predicted and actual trajectories due to its computational efficiency and simplicity. While the Frechet distance is a robust measure for trajectory similarity, it requires significantly more computational resources. MAE quantifies the average size of discrepancies between projected values and actual observations inside a model. The calculating formula is as follows:  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}|\hat{y}_{i}-y_{i}| \tag{13}\]  
\(MAE\) is the mean absolute error. \(n\) is the sample size. \(\hat{y}_{i}\) is the predicted value for the \(i\)-th observation. \(y_{i}\) is the actual value for the \(i\)-th observation. This work aims to examine the impact of environmental information on the model. To achieve this, we propose a model called LSTM-basic. Furthermore, we investigate its impact in contrast to cutting-edge GAN models. The LSTM-basic model is employed to \begin{table}
\begin{tabular}{c c} \hline \hline
**Configuration** & **Type Model/Parameter** \\ \hline Processor & Intel Core i5 9400F \\ Graphics Card & Nvidia Tesla P100 16 GB \\ Memory & 64 GB DDR4 \\ Hard Disk & 1T SSD \\ Operating System & Ubuntu 18.04 \\ Deep Learning Framework & Pytorch 2.1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Configuration of experiment. extract trajectory features. It comprises two LSTM units: an Attention Mechanism Unit and a Fully Connected Unit. During the model operation, the time and motion parameters are inputted into two distinct LSTM units for individual processing, and then the outputs of both units are combined. The feature data that have been spliced are then subjected to further processing using the Attention Mechanism Unit and the Fully Connected unit, resulting in the generation of the final output. GANs are composed of a generator and a discriminator. The generator generates simulated trajectory data, while the discriminator attempts to differentiate between real trajectory data and generated trajectory data. We choose MSELoss as the loss function for the continuous action space in order to quantify the discrepancies between the model's predictions and the actual values. During the training process, other components that share the same structure can utilise the identical model weights. It enhances the model's resilience in handling various data circumstances. Detailed information of the parameters of different models is shown in Table 3. The training outcomes are displayed in Table 4.  
The LSTM-basic model has an MAE of 102.9, the AnimalEnvnet model has an MAE of 28.4, and the GANs have an MAE of 55.5. The AnimalEnvnet model exhibits the lowest MAE value, suggesting that it has less average error in its predictions and is more accurate in estimating the real values. AnimalEnvNet has both a lower initial MAE and a faster convergence rate compared to the other models. By around round 45, the performance of the model reaches a stable MAE of around 40. In contrast, the LSTM-basic and GAN models stabilise at higher MAE values of approximately 110 and 70, respectively. The LSTM-basic model has a loss of 0.0021, while the AnimalEnvnet model has a loss of 0.0002. The AnimalEnvnet model demonstrates improved data fitting during the training process, exhibiting reduced discrepancy between its projected and actual values. Regarding training speed, the AnimalEnvNet model, however, reached a stable performance level around the 50 th batch. Although AnimalEnvNet requires a longer training period, its ultimate performance is significantly superior, achieving an average training duration of approximately 49 s per batch. Figure 7 displays a comparison of the MAE results for the models. Figure 8 displays a comparison of the outcomes obtained from the MSELoss model.