A multi‐armed bandit (MAB) algorithm is a sequential experimentation procedure on multiple treatments, which explores their effects and exploits the seemingly optimum treatment. An algorithm is selected for a particular context by evaluating the performances of multiple candidate algorithms in controlling the regret of exploration versus exploitation during the course of experimentation. We present visualization methods, and a corresponding R Shiny app for their execution, that can yield insights into the performances of popular MAB algorithms. Our visualizations illuminate an algorithm's dynamics in terms of its inferences and assignments of the arms, which govern its exploration‐exploitation trade‐off, as well as its regret behaviors. The constructions of the visualizations in our app facilitate an understanding of complicated MAB algorithms, so that the app can serve as a unique and interesting pedagogical tool for students and instructors of experimental design. We illustrate the utility of our visualizations and app using three popular MAB algorithms in the context of a binomial bandit problem.