A method of controlling a yaw offset of a first wind turbine (201a, 201e, 201i, 201m), the method comprising: receiving data indicative of a current state of the first wind turbine (201a, 201e, 201i, 201m) and of a current state of a second wind turbine (201b, 201f, 201j, 201n) adjacent to the first wind turbine (201a, 201e, 201i, 201m) downstream along a wind direction; determining one or more controlling actions associated with the yaw offset of the first wind turbine (201a, 201e, 201i, 201m) based on the current state of the first wind turbine (201a, 201e, 201i, 201m), the current state of the second wind turbine (201b, 201f, 201j, 201n), 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 (201a, 201e, 201i, 201m), 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 (201a, 201e, 201i, 201m) and of a current state of a second wind turbine (201b, 201f, 201j, 201n) adjacent to the first wind turbine (201a, 201e, 201i, 201m) downstream along a wind direction; determining one or more controlling actions associated with the yaw offset of the first wind turbine (201a, 201e, 201i, 201m) based on the current state of the first wind turbine (201a, 201e, 201i, 201m), the current state of the second wind turbine (201b, 201f, 201j, 201n), 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 (201a, 201e, 201i, 201m), 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.