We initiate a study of learning and testing dynamic environments,focusing on environment that evolve according to a fixed local rule.The (proper) learning task consists of obtaining the initial configurationof the environment, whereas for non-proper learning it suffices to predictits future values. The testing task consists of checking whetherthe environment has indeed evolved from some initial configurationaccording to the known evolution rule.We focus on the temporal aspect of these computational problems,which is reflected in the requirement that only a small portionof the environment is inspected in each time slot(i.e., the time period between two consecutive applicationsof the evolution rule).