  The procedural generation of video game levels has existed for at least 30
years, but only recently have machine learning approaches been used to generate
levels without specifying the rules for generation. A number of these have
looked at platformer levels as a sequence of characters and performed
generation using Markov chains. In this paper we examine the use of Long
Short-Term Memory recurrent neural networks (LSTMs) for the purpose of
generating levels trained from a corpus of Super Mario Brothers levels. We
analyze a number of different data representations and how the generated levels
fit into the space of human authored Super Mario Brothers levels.
