It is consequently important to choose appropriate weighting factors in the FCS-MPC's cost function, reflecting the relative importance of each control objective, as the accuracy of these factors significantly influences the overall performance of the control strategy. The online calculation of the weights in MPC's cost function is highly challenging due to the complexity and coupling between multiple control objectives [19]. Furthermore, online solutions often use observer-based approaches to dynamically optimize the weighting coefficients, but these methods significantly increase the computational burden on MPC-based systems [20; 21]. To address this, researchers have proposed alternative methods, such as using a symmetric cost function, where each term has equal priority and focuses solely on tracking errors [17]. Another approach is the use of sequential model predictive control (SMPC), which decomposes multiple control objectives into separate sub-cost functions, evaluated sequentially [22]. This method simplifies the control structure, reduces computational load, and offers flexibility. However, SMPC performance is dependent on the execution order of the sub-cost functions, making it difficult to guarantee optimal performance and preventing simultaneous optimization of all targets [23]. Furthermore, offline tuning schemes have been proposed in the literature [24]. While these schemes are straightforward, they are time-consuming and computationally expensive. Additionally, using a fixed weighting factor does not ensure optimal performance across various operating conditions. Recently, data-driven strategies have become more popular in the field of power conversion applications. It has been applied successfully in power electronic systems for model-free control approaches, particularly with the use of an artificial neural network (ANN)-based controller and estimator for the identification and control of power electronics and motor drives [25; 26]. Furthermore, data-driven schemes are used for the design of weighting factors [27]. Intelligent approaches for optimizing cost functions include heuristic methods and fuzzy logic, which often increase computational complexity. Heuristic approaches such as genetic algorithms (GAs) have been applied to optimize weighting factors in MPC, but they significantly increase computational complexity due to their iterative nature [28]. Similarly, fuzzy decision-making models, which replace the cost function to bypass weighting factor selection, are mainly suitable for online optimization and add complexity, particularly in multilevel inverters. Although both methods can improve accuracy, they impose a high computational burden on MPC systems [29]. However, data-driven designs based on ANN give a significant advantage due to their strong capabilities in nonlinear regression, prediction, and classification [30; 31]. Data-driven design reduces computational complexity, as the training and testing of the model is done offline, and when integrated online, it dynamically updates the weighting factors of the cost function to enhance the performance of the MPC, which is better than fixed weighting factors in the cost function. This paper then presents a novel method for online optimization of weighting factors in the asymmetric cost function of an FCS model predictive current controller (MPCC), targeting reduced computational complexity and enhanced dynamic performance. The approach leverages a twin delayed deep deterministic policy gradient (TD3) [32] reinforcement learning (RL) agent to formulate an optimal policy for weighting factor design, with plant dynamics and inverter nonlinearities modeled within the control environment. Through agent-environment interaction, the TD3 agent adaptively optimizes the weighting factors by maximizing a reward function, effectively addressing multi-objective control requirements.