A novel cooperative framework of learning automata (LA) is presented in this paper. In a system, where different LA can be integrated via the proposed framework, current State of Learning (SoL) index is advocated to evaluate the learning status of individual LA. Based on that learning status, individual LA will adaptively choose an appropriate interaction strategy at cooperative learning phase. Theoretical analysis demonstrated that this index is able to preserve the ε-optimal feature of independent learning automata. Experimental simulations validated that this cooperative framework is effective in improving learning speed of a variety of LA embedded in this framework. Then, we also present an application of the proposed cooperative framework in tutorial-like systems. Compared with existing method, our cooperative framework based method outperforms in both speed and accuracy.