In this study, we investigate the reaction pathway finding problem, that is, the efficient identification of a kinetically feasible reaction pathway connecting given reactant and product, each of which is represented by an equilibrium structure (EQ) node. To solve this problem, reinforcement learning algorithms possessing various search policies are constructed and their statistical performances are evaluated using the benchmark dataset of large-scale chemical reaction path networks. In Section 2, an overview of the dataset and our reinforcement learning algorithm is provided. In Section 3, various node selection policies for reaction path sampling are explained. In Section 4, algorithmic performances are evaluated based on the number of iterations required to find the goal of the product node, and the node selection mechanisms closely related to the goal finding are characterized, offering a more detailed discussion of the algorithmic performances. Finally, advanced aspects, such as hierarchical reinforcement learning and multi-objective optimization, are discussed to deepen the problem setting. Conclusions are provided in Section 5.