Dynamic programming is a computational approach that helps decision-makers find optimal strategies in sequential or dynamic games by breaking down the decision-making process into smaller steps and solving for the best course of action at each stage of the interactions. The key issue here is that the standard dynamic programming principle may not apply as the state of mean-field-type game involves both state and mean-field terms. Therefore, the dynamic programming principle needs to be extended to the space of measures.