  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
in domains such as games. Despite its potential, RL is still not widely used
for turbulence modeling and is primarily used for flow control and optimization
purposes. A new RL wall model (WM) called VYBA23 is developed in this work,
which uses agents dispersed in the flow near the wall. The model is trained on
a single Reynolds number ($Re_\tau = 10^4$) and does not rely on high-fidelity
data, as the back-propagation process is based on a reward rather than output
error. The states of the RLWM, which are the representation of the environment
by the agents, are normalized to remove dependence on the Reynolds number. The
model is tested and compared to another RLWM (BK22) and to an equilibrium wall
model, in a half-channel flow at eleven different Reynolds numbers ($Re_\tau
\in [180;10^{10}]$). The effects of varying agents' parameters such as actions
range, time-step, and spacing are also studied. The results are promising,
showing little effect on the average flow field but some effect on wall-shear
stress fluctuations and velocity fluctuations. This work offers positive
prospects for developing RLWMs that can recover physical laws, and for
extending this type of ML models to more complex flows in the future.
