Dynamic uncontrolled human-robot interaction requires robots to be able to adapt to changes in the human's behavior and intentions. Among relevant signals, non-verbal cues such as the human's gaze can provide the robot with important information about the human's current engagement in the task, and whether the robot should continue its current behavior or not. In a previous work [1] we proposed an active exploration algorithm for reinforcement learning where the reward function is the weighted sum of the human's current engagement and variations of this engagement (so that a low but increasing engagement is rewarding). We used a structured (parameterized) continuous action space where a meta-learning algorithm is applied to simultaneously tune the exploration in discrete and continuous action space, enabling the robot to learn which discrete action is expected by the human (e.g. moving an object) and with which velocity of movement. In this paper we want to show the performance of the algorithm to a simulated humanrobot interaction task where a practical approach is followed to estimate human engagement through visual cues of the head pose. We then measure the adaptation of the algorithm to engagement perturbations simulated as changes in the optimal action parameter and we quantify its performance for variations in perturbation duration and measurement noise.