  We propose a framework for developing wall models for large-eddy simulation
that is able to capture pressure-gradient effects using multi-agent
reinforcement learning. Within this framework, the distributed reinforcement
learning agents receive off-wall environmental states including pressure
gradient and turbulence strain rate, ensuring adaptability to a wide range of
flows characterized by pressure-gradient effects and separations. Based on
these states, the agents determine an action to adjust the wall eddy viscosity,
and consequently the wall-shear stress. The model training is in situ with
wall-modeled large-eddy simulation grid resolutions and does not rely on the
instantaneous velocity fields from high-fidelity simulations. Throughout the
training, the agents compute rewards from the relative error in the estimated
wall-shear stress, which allows the agents to refine an optimal control policy
that minimizes prediction errors. Employing this framework, wall models are
trained for two distinct subgrid-scale models using low-Reynolds-number flow
over periodic hills. These models are validated through simulations of flows
over periodic hills at higher Reynolds numbers and flow over the Boeing
Gaussian bump. The developed wall models successfully capture the acceleration
and deceleration of wall-bounded turbulent flows under pressure gradients and
outperform the equilibrium wall model in predicting skin friction.
