Over the past decades, MPPT has been the subject of extensive research, with traditional methods such as Perturb and Observe (P&O) and Incremental Conductance (InC) being widely adopted due to their simplicity and low computational cost. Nevertheless, these algorithms are known to suffer from several limitations, particularly under rapidly changing irradiance or partial shading conditions, where they tend to converge to local maxima or induce persistent oscillations around the operating point [3; 4]. To overcome these limitations, the integration of intelligent control techniques has gained significant attention. Among these, Artificial Neural Networks (ANNs) and Deep Q-Networks (DQNs), a type of Deep Reinforcement Learning (DRL), have shown remarkable performance in handling the complex, non-linear, and dynamic behavior of PV systems. These AI-based approaches offer faster convergence, improved tracking precision, and greater stability under challenging conditions such as partial shading. By learning from environmental variations and adapting their control strategies in real time, ANN and DQN algorithms significantly enhance the system's ability to identify and follow the true GMPP, even in highly variable scenarios. The work by [PERSON] and [PERSON] [5] presents an MPPT strategy using Artificial Neural Networks (ANNs), comparing it against the conventional P&O method. The study revealed that the ANN-based approach provides more accurate tracking and faster response times under dynamic irradiance conditions, outperforming P&O in terms of efficiency and stability. In a more recent study, [PERSON] et al. [6] applied Deep Reinforcement Learning (DRL), specifically a Deep Q-Network (DQN), for MPPT in real PV systems. The work includes both simulation and experimental validation, showing that, while DQN outperformed P&O in simulations, especially under partial shading conditions (PSCs), the real-world results were mixed. Notably, in PSC scenarios, P&O often got stuck in a local MPP, whereas DQN was able to extract up to 63.5% more energy, demonstrating the advantages of autonomous learning strategies. An early contribution to the use of reinforcement learning in MPPT is presented in the study by [PERSON] et al. [7]. In their work, a tabular Q-learning algorithm was developed to track the MPP, showing strong convergence stability and reduced computational effort when compared to traditional metaheuristic techniques. Their results demonstrated that the method consistently outperformed the conventional P&O algorithm across multiple scenarios involving variations in irradiance and temperature. This line of research has since been extended by authors such as [PERSON] [8] and [PERSON] et al. [9], who investigated alternative tabular reinforcement learning strategies for MPPT. These studies introduced comparative analyses between Q-learning and SARSA agents, as well as hybrid schemes incorporating fuzzy logic and sliding mode control, further validating the effectiveness of RL-based methods under dynamic environmental conditions. Similarly, [PERSON] et al. [10] explored DRL methods, including DQN and Deep Deterministic Policy Gradient (DDPG), for MPPT in MATLAB/Simulink simulations. Their work focused on the challenges of partial shading and showed that DQN significantly improved tracking efficiency compared to P&O. Under uniform conditions, DQN achieved a 5.83% efficiency gain, while DDPG yielded a 3.21% improvement, supporting the applicability of DRL in optimizing PV performance. [PERSON] and [PERSON] [11] presented a comparative analysis between traditional P&O and a fuzzy logic-enhanced version (FLP&O). Using a system with five series-connected PV panels and a boost converter, the study demonstrated that FLP&O offers quickerconvergence to the MPP under rapid irradiance changes, confirming the benefits of fuzzy logic in improving MPPT response time and accuracy. Furthermore, the comprehensive review by [PERSON] et al. [12] highlighted the limitations of conventional MPPT algorithms like P&O under partial shading.