Dungeon level generation using generative adversarial network: an experimental study for top-down view games

  • Daniele Fernandes e Silva UFPel / IFFar
  • Rafael Piccin Torchelsen UFPel
  • Marilton Sanchotene de Aguiar UFPel


Manual designing levels for games is a complex task, often demanding time and effort from the game designer. An option for this is using algorithms to generate such levels, improving its scalability. Currently, such procedural content generation methods can be guided by hand-crafted rules or, as in more recent approaches, by learning from existing data. In deep learning, generative adversarial networks have been proven to effectively generate samples for a given distribution in an unsupervised manner. Unfortunately, the training and usage of GANs show many challenges, such as convergence problems, instability, and mode collapse. In addition to these problems, we show that learning to generate valid levels is not trivial. In this paper, we demonstrate an empirical analysis of the use of GANs, pointing out the best state-of-the-art practices and highlighting future directions in this research field.


Adadi, A. (2021). A survey on data-efficient algorithms in big data era. Journal of Big Data, 8(1):24.

Arjovsky, M., Chintala, S., and Bottou, L. (2017). Wasserstein gan.

Barman, N., Zadtootaghaj, S., Schmidt, S., Martini, M. G., and Möller, S. (2018). Gamingvideoset: a dataset for gaming video streaming applications. In 2018 16th Annual Workshop on Network and Systems Support for Games (NetGames), pages 1–6. IEEE.

Chen, Z. and Lyu, D. (2022). Procedural generation of virtual pavilions via a deep convolutional generative adversarial network. Computer Animation and Virtual Worlds, 33(3-4):e2063.

Choi, Y., Uh, Y., Yoo, J., and Ha, J.-W. (2020). Stargan v2: Diverse image synthesis for multiple domains. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8188–8197.

Giacomello, E., Lanzi, P. L., and Loiacono, D. (2018). Doom level generation using generative adversarial networks. In 2018 IEEE Games, Entertainment, Media Conference (GEM), pages 316–323. IEEE.

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2020). Generative adversarial networks. Communications of the ACM, 63(11):139–144.

Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2, NIPS’14, page 2672–2680, Cambridge, MA, USA. MIT Press.

Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al. (2018). Recent advances in convolutional neural networks. Pattern recognition, 77:354–377.

Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. (2017). Improved training of wasserstein gans.

Gutierrez, J. and Schrum, J. (2020). Generative adversarial network rooms in generative graph grammar dungeons for the legend of zelda. In 2020 IEEE Congress on Evolutionary Computation (CEC), pages 1–8. IEEE.

Li, Z., Liu, F., Yang, W., Peng, S., and Zhou, J. (2021). A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems.

Liu, J., Snodgrass, S., Khalifa, A., Risi, S., Yannakakis, G. N., and Togelius, J. (2021). Deep learning for procedural content generation. Neural Computing and Applications, 33(1):19–37.

Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y. (2018). Spectral normalization for generative adversarial networks. arXiv preprint arXiv:1802.05957.

Padilha, R. F. (2022). Analisando o engajamento de jogadores através de mapas procedurais. Trabalho de conclusão de curso (bacharelado em ciência da computação), Centro de Desenvolvimento Tecnológico, Universidade Federal de Pelotas, Pelotas.

Ping, K. and Dingli, L. (2020). Conditional convolutional generative adversarial networks based interactive procedural game map generation. In Future of Information and Communication Conference, pages 400–419. Springer.

Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434.

Summerville, A., Snodgrass, S., Guzdial, M., Holmgård, C., Hoover, A. K., Isaksen, A., Nealen, A., and Togelius, J. (2018). Procedural content generation via machine learning (pcgml). IEEE Transactions on Games, 10(3):257–270.

Torrado, R. R., Khalifa, A., Green, M. C., Justesen, N., Risi, S., and Togelius, J. (2020). Bootstrapping conditional gans for video game level generation. In 2020 IEEE Conference on Games (CoG), pages 41–48. IEEE.

Viana, B. M. F. and dos Santos, S. R. (2021). Procedural dungeon generation: A survey. Journal on Interactive Systems, 12(1):83–101.

Wu, H., Zheng, S., Zhang, J., and Huang, K. (2017). Gp-gan: Towards realistic high-resolution image blending.

Zhang, H., Fontaine, M., Hoover, A., Togelius, J., Dilkina, B., and Nikolaidis, S. (2020). Video game level repair via mixed integer linear programming. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, volume 16, pages 151–158.

Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., and Metaxas, D. (2016). Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks.

Zhu, J.-Y., Park, T., Isola, P., and Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks.

Ziviani, H. E., Chávez, G. C., and Silva, M. C. (2022). Applying a conditional gan for bone suppression in chest radiography images. In Anais do XLIX Seminário Integrado de Software e Hardware, pages 25–36. SBC.
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FERNANDES E SILVA, Daniele; TORCHELSEN, Rafael Piccin; AGUIAR, Marilton Sanchotene de. Dungeon level generation using generative adversarial network: an experimental study for top-down view games. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 50. , 2023, João Pessoa/PB. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 95-106. ISSN 2595-6205. DOI: https://doi.org/10.5753/semish.2023.229905.