LSTM is commonly employed as a distinct form of architecture in recurrent neural networks [22; 23]. [PERSON] et al. [17] employed generative adversarial networks (GANs) along with LSTM and CNNs to create generators and discriminators. They conducted experiments using various structure combinations on a dataset of seabird foraging trajectories. Among them, GANs composed of CNN-CNN achieved the best results. However, GANs still fall short in capturing local-scale descriptive statistics, such as step-speed distributions. [PERSON] et al. [21] developed a comprehensive paradigm for forecasting animal locomotion that may be used universally. Their research involved a comparative analysis of single-step and multi-step long-term prediction outcomes using random forests, neural networks, and recurrent neural networks. The findings revealed that deep learning techniques exhibited superior performance in single-step prediction, whereas traditional models demonstrated better results in multi-step long-term prediction. Out of all the deep learning models, LSTM had the highest performance in multi-step long-term prediction. Constructing machine learning and deep learning models is more straightforward compared to classic parametric motion models, as the latter sometimes rely on limiting assumptions. Nevertheless, the interpretability of machine learning and deep learning models is more challenging compared to parametric motion models. Remote sensing images are visual representations of surface data acquired using remote sensing technologies. The Earth's surface is remotely viewed and recorded using sensors deployed on platforms such as satellites, aircraft, drones, and other similar devices. Both classic machine learning algorithms and deep learning models face a common challenge: they are unable to incorporate complex environmental variables, particularly unstructured data in the form of remote sensing images. Remote sensing images include abundant information about the surrounding environment, which can be incorporated as environmental variables into animal movement models. The challenge lies in extracting valuable information from remote sensing images in an efficient manner. Reinforcement learning is a machine learning approach that acquires optimum judgements via iterative interactions with the environment [24; 25]. This learning technique involves an agent making decisions based on the current state at each time step and modifying its approach depending on feedback rewards from the environment in order to maximise the total rewards over the long run [26]. The advancement of deep learning-based technologies in remote sensing images has facilitated the creation of agents that can replicate animal movement behaviour [27; 28; 29; 30]. The progress of these technologies has increased the ease of obtaining animal trajectory data and environmental data. Wildlife monitoring technology, such as GPS collars, tags, and acoustic localisation, are extensively used [31]. Furthermore, drones and satellite remote sensing images have the capability to provide intricate and precise surface data [32]. Agent-based animal movement models enable the adoption of an individual-based approach to manipulate important variables, such as internal states, external stimuli, locomotion skills, and navigation abilities [33; 34]. This allows for a more thorough investigation of the underlying mechanisms that influence animal behaviour. However, such models still face a number of challenges and problems, such as the complexity of animal movement and interaction with the environment [35]. In order to tackle the difficulties encountered in modelling wildlife behaviour, it is crucial to investigate novel ways for simulating animal behaviour. Specifically, agent models that can include various data sources and replicate animal movement behaviour are of the utmost importance. The main contributions of this study are as follows:  
1.