The close proximity of wind turbines to one another in a wind farm can lead to inefficiency in terms of power production due to wake effects. One technique to mitigate the losses is to veer from their individual optimal direction. As such, the wakes can be steered away from downstream turbines in order to increase the overall power output. Multi-Agent Reinforcement Learning (MARL) models the interactions between wind turbines and determines an optimal control strategy through agents that learn the collective consequences of their actions. To analyse the benefit of multi-agent cooperation and centralised critic evaluation, I investigated the effect of Counterfactual Multi-Agent Policy Gradients (COMA) on Active Wake Control. Ultimately, experiments on wind farms of three and sixteen turbines indicate that the algorithm performs moderately, yet worse than single-agent Reinforcement Learning. In addition, high computation costs hinder its application on real-life environments.