A stationary environment refers to data-generating distributions that do not change over time.

A non-stationary environment, in contrast, refers to data-generating distributions that do change over time.

It is a difficult problem to train machine learning algorithms to generalize well in non-stationary environments. See Machine Learning in Non-Stationary Environments for more information.