## 1 Introduction  
Wildfires have become an increasingly prevalent global threat, exacerbated by climate change, rising temperatures, and prolonged drought periods [1]. The frequency and intensity of these fires have escalated in recent decades, resulting in severe ecological, economic, and human losses [2]. Countries in Latin America, particularly Chile, experience recurrent and devastating wildfire seasons, significantly affecting biodiversity, air quality, and local communities [3]. The expansion of human settlements into fire-prone areas has further increased the vulnerability of populations to wildfire hazards [4]. Accurate wildfire prediction is essential to mitigate their devastating effects, improve early warning systems, and optimize resource allocation for fire suppression [5]. Traditional wildfire prediction models rely on statistical methods and machine learning techniques such as logistic regression, decision trees, and gradient boosting [6]. Although these models provide valuable information, they struggle to capture the complex, nonlinear interactions between the environmental factors that influence wildfire spread [7]. Recent advances in deep learning, particularly Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, have shown promising results in wildfire risk assessment [8]. However, these models often require extensive labeled data and face challenges adapting to non-stationary environmental dynamics [9]. Deep Reinforcement Learning (DRL) has emerged as a powerful alternative, capable of learning optimal prediction strategies through interaction with simulated wildfire environments [10]. The Actor-Critic DRL framework, in particular, combines policy-based and value-based learning, enabling adaptive decision-making in dynamic conditions [11]. Given the high incidence and impact of wildfires in Chile and Latin America, this study explored the effectiveness of DRL-based wildfire prediction models compared to traditional deep learning approaches, assessing their predictive accuracy, adaptability, and computational feasibility. Recent studies have explored DRL for wildfire simulation and suppression, such as simulation-based approaches for dynamic fire behavior modeling [11; 12]. These works support the potential of DRL for adaptive strategies in real-time decision-making, motivating its comparison against more traditional models in this study. The primary objective of this research was to evaluate the efficacy of DRL-based wildfire prediction models, specifically focusing on the Actor-Critic architecture. The following specific objectives outline the work in this paper:  
* Develop and implement a DRL-based Actor-Critic model for wildfire prediction using historical environmental data. * Compare the performance of DRL models with traditional deep learning approaches, including CNNs and Transformer-based architectures. * Analyze the impact of different environmental features (temperature, wind speed, vegetation type, and topography) on wildfire occurrences using feature importance analysis. * Propose a practical framework for integrating DRL models into operational wildfire management systems, to enhance predictive accuracy and decision-making capabilities. This study is particularly relevant in the Latin American and Chilean contexts, due to the high occurrence of wildfires and their impact in these regions. The extensive forested landscapes and changing climate patterns create conditions conducive to frequent and large-scale wildfires [13]. Chile's government agencies and emergency response teams, such as the National Forestry Corporation (CONAF), require advanced predictive tools to improve wildfire preparedness and response strategies [14]. Through AI-driven wildfire prediction models, this research contributes to developing proactive fire management strategies, to minimize environmental destruction and human casualties.