research-article

Generating and Blending Game Levels via Quality-Diversity in the Latent Space of a Variational Autoencoder

Authors:

Seth CooperAuthors Info & Claims

FDG '21: Proceedings of the 16th International Conference on the Foundations of Digital Games

Article No.: 3, Pages 1 - 11

https://doi.org/10.1145/3472538.3472545

Published: 21 October 2021 Publication History

Abstract

Several works have demonstrated the use of variational autoencoders (VAEs) for generating levels in the style of existing games and blending levels across different games. Further, quality-diversity (QD) algorithms have also become popular for generating varied game content by using evolution to explore a search space while focusing on both variety and quality. To reap the benefits of both these approaches, we present a level generation and game blending approach that combines the use of VAEs and QD algorithms. Specifically, we train VAEs on game levels and run the MAP-Elites QD algorithm using the learned latent space of the VAE as the search space. The latent space captures the properties of the games whose levels we want to generate and blend, while MAP-Elites searches this latent space to find a diverse set of levels optimizing a given objective such as playability. We test our method using models for 5 different platformer games as well as a blended domain spanning 3 of these games. We refer to using MAP-Elites for blending as Blend-Elites. Our results show that MAP-Elites in conjunction with VAEs enables the generation of a diverse set of playable levels not just for each individual game but also for the blended domain while illuminating game-specific regions of the blended latent space.

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Cited By

Silva DTorchelsen RAguiar M(2025)Procedural game level generation with GANs: potential, weaknesses, and unresolved challenges in the literatureMultimedia Tools and Applications10.1007/s11042-025-20612-9Online publication date: 18-Jan-2025
https://doi.org/10.1007/s11042-025-20612-9
Withington OCook MTokarchuk L(2024)On the Evaluation of Procedural Level Generation SystemsProceedings of the 19th International Conference on the Foundations of Digital Games10.1145/3649921.3650016(1-10)Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3649921.3650016
Guo JChristodoulou ALaczko BGlette KLi XHandl J(2024)LVNS-RAVE: Diversified audio generation with RAVE and Latent Vector Novelty SearchProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3654432(667-670)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3654432
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FDG '21: Proceedings of the 16th International Conference on the Foundations of Digital Games

August 2021

534 pages

ISBN:9781450384223

DOI:10.1145/3472538

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 21 October 2021

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FDG'21

FDG'21: The 16th International Conference on the Foundations of Digital Games 2021

August 3 - 6, 2021

QC, Montreal, Canada

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Cited By

Silva DTorchelsen RAguiar M(2025)Procedural game level generation with GANs: potential, weaknesses, and unresolved challenges in the literatureMultimedia Tools and Applications10.1007/s11042-025-20612-9Online publication date: 18-Jan-2025
https://doi.org/10.1007/s11042-025-20612-9
Withington OCook MTokarchuk L(2024)On the Evaluation of Procedural Level Generation SystemsProceedings of the 19th International Conference on the Foundations of Digital Games10.1145/3649921.3650016(1-10)Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3649921.3650016
Guo JChristodoulou ALaczko BGlette KLi XHandl J(2024)LVNS-RAVE: Diversified audio generation with RAVE and Latent Vector Novelty SearchProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3654432(667-670)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3654432
Sudhakaran SGonzález-Duque MFreiberger MGlanois CNajarro ERisi SOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)MarioGPTProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668483(54213-54227)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3668483
Allard MSmith SChatzilygeroudis KLim BCully A(2023)Online Damage Recovery for Physical Robots with Hierarchical Quality-DiversityACM Transactions on Evolutionary Learning and Optimization10.1145/35969123:2(1-23)Online publication date: 28-Jun-2023
https://dl.acm.org/doi/10.1145/3596912
Sudhakaran SGonzález-Duque MGlanois CFreiberger MNajarro ERisi SSilva SPaquete L(2023)Prompt-Guided Level GenerationProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3590656(179-182)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3590656
Mirgati NGuzdial M(2023)Joint Level Generation and Translation Using Gameplay Videos2023 IEEE Conference on Games (CoG)10.1109/CoG57401.2023.10333193(1-10)Online publication date: 21-Aug-2023
https://doi.org/10.1109/CoG57401.2023.10333193

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