Autoencoder and Evolutionary Algorithm for Level Generation in Lode Runner

Sarjak Thakkar, Changxing Cao, Lifan Wang, Tae Jong Choi, Julian Togelius

Abstract: Procedural content generation can be used to create arbitrarily large amounts of game levels automatically, but traditionally the PCG algorithms needed to be developed or adapted for each game manually. Procedural Content Generation via Machine Learning (PCGML) harnesses the power of machine learning to semi-automate the development of PCG solutions, training on existing game content so as to create new content from the trained models. One of the machine learning techniques that have been suggested for this purpose is the autoencoder. However, very limited work has been done to explore the potential of autoencoders for PCGML. In this paper, we train autoencoders on levels for the platform game Lode Runner, and use them to generate levels. Compared to previous work, we use a multi-channel approach to represent content in full fidelity, and we compare standard and variational autoencoders. We also evolve the values of the hidden layer of trained autoencoders in order to find levels with desired properties.