## 1 Introduction  
Wind energy gains strength year after year. This renewable energy is becoming one of the most used clean energies worldwide due to its high efficiency, its competitive payback, and the growth in investment in sustainable policies in many countries [1]. Despite its recent great development, there are still many engineering challenges regarding wind turbines (WT) technology that must be addressed [2]. From the control perspective, one of the main goals is to stabilize the output power of the WT around its rated value. This should be achieved while the efficiency is maximized, and vibrations and fatigue are minimized. Even more, safety must be guaranteed under all operation conditions. This may even be more critical for floating offshore wind turbines (FOWT), as it has been proved that the control system can affect the stability of the floating device [3, 4]. This general and ambitious control objective is implemented in many different control actions, depending on the type of WT. So, the pitch angle control is normally used to maintain the output power close to its rated value once the wind speed overpasses a certain threshold. The control of the generator speed is intended to track the optimum rotor velocity when the wind speed is below the rated output speed. And finally, the yaw angle control is used to optimize the attitude of the nacelle to follow the wind stream direction. This work is focused on the pitch control of the wind turbine. Blade pitch control technology alters the pitch angle of a blade to change the angle of wind attack and ultimately to change the aerodynamic forces on the blades of a WT. Therefore, this control system pitches the blades usually a few degrees every time the wind changes in order to keep the rotor blades at the required angle, thus controlling the rotational speed of the turbine [5,6]. This is not a trivial task due to the non-linearity of the equations that describe its dynamics, the coupling between the internal variables, and uncertainty that comes from external loads [7], mainly wind and, in the case of FOWT, also waves and currents, that makes its dynamics changes [8]. The complexity of this system has led to propose the use of advanced and intelligent control techniques such as fuzzy logic, neural networks, and reinforcement learning, among others, to address WT related control problems. Among control solutions, sliding mode control, and adaptive control have been recently applied to WT with encouraging results. In [PERSON] et al. [9], a PI-type sliding mode control (SMC) strategy for (permanent magnet synchronous generator) PMSG-based wind energy conversion system (WECS) uncertainties is presented. [PERSON] et al. propose a super-twisting sliding mode control for a gearless wind turbine by a permanent magnet synchronous generator [10]. A robust SMC approach to control the rotor speed in the presence of uncertainties in the model is also proposed in [PERSON] et al. [11]. In addition, closed loop convergence of the whole system is proved. In [PERSON] et al. [12], an adaptive robust integral SMC pitch angle controller and a projection-type adaptation law are synthesized to accurately track the desired pitch angle trajectory, while it compensates model uncertainties and disturbances. [PERSON] et al. propose an adaptive controller for pitch and torque control of the wind turbines operating under turbulent wind conditions [13]. The WT control problem has also been addressed in the literature using different intelligent control techniques, mainly fuzzy logic and neuro-fuzzy inference systems [14, 15, 16, 17, 18, 19, 20, 21]. Reinforcement learning has been an inspiration for the design of control strategies [22,23]. A recent overview of deep reinforcement learning for power system applications can be found in [PERSON] et al. [24]. In [PERSON] et al. [25], RL is used for the control of variable speed wind turbines. Particularly, it adapts conventional variable speed WT controllers to changing wind conditions.