The expected shortage in spectrum supply is well understood to be primarily due to the inefficient, static nature of current spectrum allocation policies. In order to address this problem, Federal Communications Commission promotes the so called opportunistic spectrum access (OSA) to be applied on cognitive radio networks (CRNs). In short, the idea behind OSA is allowing unlicensed users to use unused licensed spectra as long as they do not cause interference to licensed users. In this paper, we present and evaluate learning schemes that allow unlicensed users to locate and use spectrum opportunities effectively, thus improving efficiency of CRNs. We separately consider two models: single and multiple unlicensed user(s). For the latter model, we present two schemes: noncooperative and cooperative Q-learning. All proposed schemes do not require prior knowledge or prediction models of the environment's dynamics and behaviors, yet can still achieve high performance by learning from interaction with the environment. Using simulations, we show that the proposed schemes achieve good performances in terms of throughput and fairness.