Spectrum sensing is one of the main functions in cognitive radio networks. To improve the sensing performance and increase spectrum efficiency, a number of cooperative spectrum sensing methods have been proposed. However, most of these methods focused on a single-channel environment. In this letter, we present a novel cooperative spectrum sensing method based on cooperator selection in a multi-channel cognitive radio network. Using reinforcement learning, a cognitive radio user can select reliable and robust cooperators, without any a priori knowledge. Using the proposed method, a cognitive radio user can achieve better sensing capability and overcome performance degradation problems due to malicious users or erratic user behavior. Numerical results show that the proposed method can achieve excellent performance.