Reinforcement learning is a type of machine learning that aims to maximize long-term outcomes through trial and error. For the reaction pathway finding problem, given a partially known reaction path network, referred to as the "environment," at each iteration step, a program with the "policy" selects the next local minimum structure for reaction path sampling from a set of candidate local minimum structures that have been acquired up to each iteration step. This process is called "action." This provides the (added) reaction path and associated local minimum structure with the "reward" to quantify the score of the obtained structure (e.g., the structural similarity to the product) thereby changing the "environment," and the "policy" is updated and refined through learning based on "rewards."