Disease gene prioritization is the process of ranking candidate genes according to their relevance to a disease phenotype, thus facilitating the identification of disease genes by narrowing down the set of genes to be tested experimentally. Many methods have been proposed for disease gene prioritization based on relationships between proteins encoded in protein-protein interaction networks using various graph-based algorithms. In this paper, we propose a novel method for prioritizing candidate disease genes by combining reinforcement learning with PageRank algorithm and assigning priors for known disease genes. We experimentally evaluate the proposed method on a human protein interaction network and compared its performance with a state-of-the-art methods, namely PageRank with priors, Random Walk with Restart and K-Step Markov. The experiment results show that our method achieves relatively high performance in terms of AUC values and outperforms comparative methods.