Methods that combine molecular dynamics simulations with reinforcement learning have been developed and primarily applied to the computation of structural changes, such as protein folding. For example, target-oriented computational approaches have been explored, where the reward is designed to decrease the root-mean-square distance to a target structure, such as a folded protein, and balances exploration and exploitation. Other studies have investigated rewards that reflect energy of conformations and order parameter to describe the direction of a target. In contrast, the development of the reinforcement learning approach to the computation of chemical reaction pathways has been limited. A related approach combines the rapidly exploring random tree (RRT) algorithm with the AFIR method. In the hybrid RRT-AFIR method, the calculation alternates between exploiting bias toward the product, based on structural similarity, and performing random explorations. Because of the high computational cost associated with reaction pathway calculations, statistical validation of probabilistic algorithms has been tremendously challenging. Fortunately, the availability of a large-scale benchmark dataset, constructed using the AFIR method, enables sufficient statistical validation of algorithmic performance and encourages the further development of reinforcement learning algorithms that can be applied to the computation of reaction pathways.