goal of using generative models to plan actions another major use of generative models is that they are able to handle missing data much more effectively than the standard input to output mappings of machine learning models that we usually use generative models are able to fill in missing inputs and they're also able to learn when some of the labels in the data set are missing semi-supervised learning is a particularly useful application of generative modeling where we may have very few labeled inputs but by leveraging many more unlabeled