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

Super Mario as a String: Platformer Level Generation Via LSTMs