We investigate in this paper many problems related to the decision-making process in the Cognitive Radio (CR), where a Secondary User (SU) tries to maximize its opportunities by finding the most vacant channel. Recently, Multi-Armed Bandit (MAB) problems attracted the attention to help a single SU, in the context of CR, makes an optimal decision using the well-known MAB algorithms, such as: Thompson Sampling, Upper Confidence Bound, e-greedy, etc. However, the big challenge for multiple SUs remains to learn collectively or separately the vacancy of channels and decrease the number of collisions among users. To solve the latter issue for multiple users, the All-Powerful Learning (APL) policy is proposed; this new policy considers the priority access and the dynamic multi-user access, where the number of SUs may change over time. Based on our APL policy, we consider as well as the Quality of Service (QoS), where SUs should estimate and then access best channels in terms of both quality and availability. The experimental results show the superiority of APL compared to existing algorithms, and it has also been shown that the SUs are able to learn channels qualities and availabilities and further enhance the QoS. © 2020 ASTES Publishers. All rights reserved.