Double Q-Learning is a technique developed to address the overestimation bias inherent in standard Q-Learning algorithms. This bias arises because Q-Learning typically selects the maximum action value during the update process, which can lead to overly optimistic estimates of the value functions. To understand how Double Q-Learning mitigates this issue, it is essential to consider