## 1 Introduction
As a further step, the same authors apply conditioned RL (CRL) to this complex control scenario with large state-action spaces to be explored [26]. In [PERSON] et al. [27], an online controller based on a policy iteration reinforcement learning paradigm along with an adaptive actor-critic technique is applied to the doubly fed induction generator wind turbines. [PERSON] proposes an adaptive PID controller tuned by RL [28]. An artificial neural network based on RL for WT yaw control is presented in [PERSON] et al. [29]. In a more recent paper, the authors propose a performance enhancement of this wind turbine neuro-RL yaw control [30]. The paper by [PERSON] proposes a RL algorithm to plan the battery scheduling in order to optimize the use of the grid [31]. [PERSON] et al. propose adaptive control techniques, which first extract the stochastic property of wind speed using a trained RL agent, and then apply their obtained optimal policy to the wind turbine adaptive control design [32]. In [PERSON] et al., passive RL solved by particle swarm optimization policy (PSO-P) is used to handle an adaptive neuro-fuzzy inference system type-2 structure with unsupervised clustering for controlling the pitch angle of a real wind turbine [33]. [PERSON] et al. also propose a robust wind turbine controller that adopts adaptive dynamic programming based on RL and system state data [34]. In a related problem, deep reinforcement learning with knowledge assisted learning are applied to deal with the wake effect in a cooperative wind farm control [35]. To summarize, in the literature, the RL approach has been applied to the different control actions of wind turbines or to other related problems with successful results. However, this learning strategy has not been directly applied to the pitch control, neither the reward mechanisms have been analyzed in order to improve the control performance. Thus, in the present work, the RL-inspired pitch control proposed in [PERSON] et al. [36] has been extended to complete the range of reward policies. Novel reward strategies related to the energy deviation from the rated power, namely Mean SquaredError Reward Strategy (MSE-RS), Mean error Reward Strategy (Mean-RS), and the corresponding increments, \(\Delta\)MSE-RS and \(\Delta\)Mean-RS, have been proposed, implemented, and combined. In addition, many of the previous works based on RL execute \(\epsilon\)-greedy methods in order to increase the exploration level and avoid actions unexplored. The \(\epsilon\)-greedy methods select an action either randomly or considering the previous experiences, the latter being called greedy selection. The greedy selection is carried out trying to maximize the future expected rewards, based on the previous rewards already received. The probability of selecting a random action is \(\epsilon\) and the probability of performing a greedy selection is \((1-\epsilon)\). This approach tends to improve the convergence of the learning, but its main drawback is that it introduces a higher randomness in the process and makes the system less deterministic. To avoid the use of \(\epsilon\)-greedy methods, the concept of Positive-Negative (P-N) rewards, and its relationship with the exploration-exploitation dilemma is here introduced and linked to the WT control problem. An advantage of P-N rewards, observed in this work, is that the behavior is generally more deterministic than \(\epsilon\)-greedy, allowing more replicable results with fewer iterations. Moreover, a deep study of the influence of the type of reward in the performance of the system response and in the learning speed has also been carried out. As it will be shown in the simulation experiments, P-N rewards work better than Only-Positive (O-P) rewards for all the policy update algorithms. However, the combination of P-N with O-P rewards helps to soften the variance of the output power. The rest of the paper is organized as follows. Section 2 describes the model of the small wind turbine used.