Leveraging Reinforcement Learning and WaveFunctionCollapse for Improved Procedural Level Generation
Mathias Babin, Michael J. Katchabaw · 2021
This work presents a novel approach to training Reinforcement Learning (RL) agents to serve as heuristics for the WaveFunctionCollapse (WFC) algorithm in the production of procedurally generated video game levels. While the algorithm’s original minimal entropy heuristic is sufficient for constructing levels that look visually appealing, it is often the case that these levels suffer when playability is concerned. The approach presented in this work involves replacing this heuristic with a set of deep neural networks trained using RL to direct the algorithm in the construction of playable levels for the original Super Mario Bros. (SMB). We evaluate the performance of our models using a game-playing A*-based agent provided by the Mario AI Competition Framework and designate a simple reward function which reflects the quality of generated levels based on the A* agent’s ability to successfully navigate them. Results using this approach show an increase in the percentage of playable levels generated using our learned heuristics over those using minimal entropy.