A ML-based approach for automating and improving the process of sequential circuits\' verification is proposed. The method employs adaptive neural network with shortest path algorithm to generate a directed sequence of tests (instructions) to improve overall coverage. The proposed method achieve coverage closure when used to verify a quad-core cache design implementing MESI protocol. The employed adaptive neural network also overcomes the problem of sensitivity of learning algorithms to the size and quality of the initial training set.