Yaw misalignment strategies can increase the power output of wind farms by mitigating wake effects, but finding optimal yaws requires overcoming both modeling errors and the growing complexity of the problem as the size of the farm grows. Recent works have therefore proposed decentralized multi-agent reinforcement learning (MARL) as a model-free, data-based alternative to learn online. These solutions have led to significant increases in total power production on experiments with both static and dynamic wind farms simulators. Yet experiments in dynamic simulations suggest that convergence time remains too long for online learning on real wind farms. As an improvement, baseline policies obtained by optimizing offline through steady-state models can be fed as inputs to an online reinforcement learning algorithm. This method however does not guarantee a smooth transfer of the policies to the real wind farm. This is aggravated when using function approximation approaches such as multi-layer neural networks to estimate policies and value functions. We propose an imitation approach, where learning a policy is first considered a supervised learning problem by deriving references from steady-state wind farm models, and then as an online reinforcement learning task for adaptation in the field. This approach leads to significant increases in the amount of energy produced over a lookup table (LUT) baseline on experiments done with the mid-fidelity dynamic simulator FAST.Farm under both static and varying wind conditions.