A method of controlling a yaw offset of a first wind turbine , the method comprising: receiving data indicative of a current state of the first wind turbine and of a current state of a second wind turbine adjacent to the first wind turbine downstream along a wind direction ; determining one or more controlling actions associated with the yaw offset of the first wind turbine based on the current state of the first wind turbine , the current state of the second wind turbine , and a reinforcement learning algorithm ; and controlling the yaw offset of the first wind turbine by applying the determined one or more controlling actions to the first wind turbine , characterized in that the reinforcement learning algorithm utilizes: an action-reward method, in which each controlling action is associated with a reference reward value, and the reinforcement learning algorithm is trained based on a comparison between the reference reward value and a current reward value; or an action-value method, in which the reinforcement learning algorithm predicts a reward value for a future point in time, based on a predefined value function, with the one or more controlling actions determined by taking into account the predicted reward value. receiving data indicative of a current state of the first wind turbine and of a current state of a second wind turbine adjacent to the first wind turbine downstream along a wind direction ; determining one or more controlling actions associated with the yaw offset of the first wind turbine based on the current state of the first wind turbine , the current state of the second wind turbine , and a reinforcement learning algorithm ; and controlling the yaw offset of the first wind turbine by applying the determined one or more controlling actions to the first wind turbine , characterized in that the reinforcement learning algorithm utilizes: an action-reward method, in which each controlling action is associated with a reference reward value, and the reinforcement learning algorithm is trained based on a comparison between the reference reward value and a current reward value; or an action-value method, in which the reinforcement learning algorithm predicts a reward value for a future point in time, based on a predefined value function, with the one or more controlling actions determined by taking into account the predicted reward value. characterized in that the reinforcement learning algorithm utilizes: an action-reward method, in which each controlling action is associated with a reference reward value, and the reinforcement learning algorithm is trained based on a comparison between the reference reward value and a current reward value; or an action-value method, in which the reinforcement learning algorithm predicts a reward value for a future point in time, based on a predefined value function, with the one or more controlling actions determined by taking into account the predicted reward value. an action-reward method, in which each controlling action is associated with a reference reward value, and the reinforcement learning algorithm is trained based on a comparison between the reference reward value and a current reward value; or an action-value method, in which the reinforcement learning algorithm predicts a reward value for a future point in time, based on a predefined value function, with the one or more controlling actions determined by taking into account the predicted reward value. <EOS>