This master thesis focuses on using reinforcement learning (RL) to generate explicit control laws for systems. The approach combines the principles of model predictive control design to obtain a control law without prior knowledge of the process model. The work begins with investigating RL concepts and the main tools for implementing RL in generating explicit control laws. Next, a series of simulations are conducted using linear dynamical models to generate the explicit control law. The performance of the RL-based approach is compared with the explicit model predictive control approach. The research then focuses on generalizing the process and developing a framework in Matlab that provides an explicit control law for a large class of different models and processes. The framework's effectiveness, efficiency, and stability are evaluated. The thesis concludes with a discussion of the results obtained and their implications for the field of control. The findings suggest that RL algorithms have the potential to generate effective control policies for a range of unknown systems, but further research is needed to address issues such as generalization to new environments and safety considerations. Overall, this work contributes to the growing research on RL-based control and lays a foundation for future investigations in this area.