This paper focuses on the use of reinforcement learning (RL) as a machine-learning (ML) modeling tool for near-wall turbulence. RL has demonstrated its effectiveness in solving high-dimensional problems, especially domains such games. Despite potential, is still not widely used turbulence and primarily flow control optimization purposes. A new wall model (WM) called VYBA23 developed this work, ...