When multiple wind turbines are positioned close to one another, such as in a wind farm, wind turbines located downwind of other turbines are not 100% efficient due to wakes, negatively affecting the total power output of the wind farm. A way to mitigate the loss of power is to steer the wake away from the next turbine, which lowers the current turbine's power output but increases the turbine's total power output. As the number of wind turbines increases, how complex it is to calculate the optimal steering increases exponentially. Reinforcement learning techniques have been a promising candidate to solve this problem. However, single-agent techniques are still very computationally expensive when the number of turbines is the same as an average wind farm. Therefore, this paper aims to see how the QMIX algorithm, a multi-agent reinforcement learning technique, can be efficiently applied to the problem of active wake control. QMIX will be compared to the FLORIS model and a single agent deep reinforcement learning technique TD3 to see if it achieves a higher average reward and converges faster. Finally, QMIX is tested on a larger wind farm to see if it achieves any results in a reasonable amount of time, showing that multi-agent reinforcement learning techniques are more suitable to the problem. This paper shows QMIX has the potential to outperform TD3; although FLORIS is better for smaller wind farms but more research has to be done in applying QMIX to the problem.