## 5 Discussion  
Simulating animal movement trajectories has long been an important area of study in the field of animal behaviour studies. Traditional approaches, such as random walk models [44] and HMMs [45], are often used in these types of research. These approaches are very effective in capturing local behavioural patterns, but they have limits when it comes to capturing complicated time and space changes [46; 47]. [PERSON] et al. [48] achieved exceptional results in simulating core feeding trajectories by using GANs. However, GANs generally focus the generation of trajectories that resemble actual data distributions, placing less attention on the acquisition of knowledge and adjustment to environmental and behavioural methods [49]. AnimalEnvNet has greater training stability compared to trajectory simulation using GANs, since GAN models are susceptible to encountering challenges of training instability. Although GAN models are capable of producing lifelike trajectories, they are limited in their capacity to adjust to changes in the environment and behavioural methods. The AnimalEnvNet, which uses reinforcement learning-trained agents, is capable of precisely imitating animal movement for a maximum of 12 steps, with an error limit of 1000 m. This showcases its ability to adapt to changes in the environment and acquire novel strategies via learning.  
While our model has shown some level of success in forecasting animal movement, it fails to provide a clear explanation for the precise impacts of environmental factors on locomotor behaviour. The primary objective of our work is to forecast movement trajectories rather than elucidating the underlying reasons driving movement. However, comprehending the impact of the environment on animal locomotion is a crucial facet of ecological and behavioural investigation. Hence, our methodology has certain constraints in this aspect.  
Our model has satisfactory performance in making forecasts for short time intervals, with an error of approximately 28.4 m. However, the accuracy of our predictions drastically diminishes when forecasting over longer time periods, with an error of around 1 km after 12 h. This implies that simulations may have limited efficacy in imitating animal motions over extended periods of time. Forecasting animal movements over extended time periods is a highly intricate undertaking that encompasses numerous aspects, including the stochastic nature of animal behaviour, fluctuations in the environment, and various sources of uncertainty. Hence, it is crucial that we engage in explicit discussions regarding the con straints and anticipated outcomes of models. While the model's accuracy diminishes when making predictions over longer time periods, it remains valuable for short-term predictions and recognising behavioural patterns. To enhance the model's long-term forecasting capability, future research may explore the incorporation of additional environmental factors and the utilisation of more advanced time-series analysis techniques. Furthermore, the implementation of the model should be customised to suit unique requirements. For instance, the model's exceptional accuracy in predicting short-term outcomes would be highly valuable for monitoring animal behaviour and responding to emergencies in wildlife reserves. Ultimately, although our method has certain limits, it remains highly valuable for studying animal locomotor behaviour and making short-term predictions. In future investigations, it is necessary to precisely delineate the practical application scenarios of the model and thoroughly investigate potential enhancements.