Synchronization in complex networks is an impactful phenomena with applications in numerous disciplines that have attracted the researcher’s attention for decades. Recently, there have been efforts to investigate brain synchronization by representing brain signals as complex networks and analyzing the dynamic interactions between neural regions to uncover patterns of connectivity and coordination. Focusing on functional Near-Infrared Spectroscopy signals converted to visibility networks, this paper incorporates reinforcement learning into the exploration of synchronization suppression in the visibility networks constructed, enhancing interventions to mitigate excessive neural synchronization. In this study, we extend the Kuramoto model by adding input signals to pinned nodes of the networks generated via the Reinforced Learning, more precisely the Proximal Policy Optimization algorithm, and analyze the synchronization suppression conditions. Comparison to results from other existing models in the literature is done via an experimental study in a realistic setting.