Exploring Level Blending across Platformers via Paths and Affordances

TL;DR

This paper presents a novel machine learning approach for generating new platformer game levels by blending features from six different games using a shared latent space.

Contribution

It introduces a new affordance and path vocabulary and trains variational autoencoders to enable cross-domain level generation in platformers.

Findings

Successful encoding of six platformer domains

Generation of levels with mixed domain features

Latent space captures diverse level styles

Abstract

Techniques for procedural content generation via machine learning (PCGML) have been shown to be useful for generating novel game content. While used primarily for producing new content in the style of the game domain used for training, recent works have increasingly started to explore methods for discovering and generating content in novel domains via techniques such as level blending and domain transfer. In this paper, we build on these works and introduce a new PCGML approach for producing novel game content spanning multiple domains. We use a new affordance and path vocabulary to encode data from six different platformer games and train variational autoencoders on this data, enabling us to capture the latent level space spanning all the domains and generate new content with varying proportions of the different domains.