Generating Lode Runner Levels by Learning Player Paths with LSTMs
This addresses the issue of generating coherent and controllable game levels for Lode Runner players and developers, but it is incremental as it builds on existing PCGML methods.
The paper tackled the problem of controllability and coherence in procedural content generation via machine learning (PCGML) by learning to generate human-like player paths using LSTMs and then creating game levels based on these paths, resulting in more coherent levels for Lode Runner compared to an existing PCGML approach.
Machine learning has been a popular tool in many different fields, including procedural content generation. However, procedural content generation via machine learning (PCGML) approaches can struggle with controllability and coherence. In this paper, we attempt to address these problems by learning to generate human-like paths, and then generating levels based on these paths. We extract player path data from gameplay video, train an LSTM to generate new paths based on this data, and then generate game levels based on this path data. We demonstrate that our approach leads to more coherent levels for the game Lode Runner in comparison to an existing PCGML approach.