With the increased demands for 5G networks and the limited radio resources, providing high spectral efficiency, low delay, low energy consumption, and other Key Performance Indicators (KPIs) is a challenging task. Extensive research has been conducted to propose efficient solutions for specific objectives and contexts. Although these solutions (often heuristicbased) are highly effective in specific contexts, their performance diminishes when applied in different conditions. This implies difficulties in adapting to environmental variations and/or changes in objectives. In order to overcome this problem, we propose an approach employing reinforcement learning to dynamically derive the formula for a scheduler that can be adapted to any context and objective. The proposed solution is validated with a Proof of Concept (PoC), which highlights the Artificial Intelligence (AI) ability to identify the adequate scheduler to optimize spectral efficiency in different traffic loads contexts.