In this article, we summarize the PCG (Procedural Content Generation) papers for game research.
- Smith, G. Understanding procedural content generation: a design-centric analysis of the role of PCG in games. CHI, 2014 [Paper]
- Liu, J., Snodgrass, S., Khalifa, A., Risi, S., Yannakakis, G. N., & Togelius, J. Deep learning for procedural content generation. Neural Computing and Applications, 2020 [Paper]
- Khalifa, A., Bontrager, P., Earle, S., & Togelius, J. Pcgrl: Procedural content generation via reinforcement learning. AIIDE, 2020 [Paper] [Code]
env:pcgrl
- Earle, S., Edwards, M., Khalifa, A., Bontrager, P., & Togelius, J. Learning Controllable Content Generators. Arxiv, 2021 [Paper]
env:pcgrl
env:simcity
env:rct
- Gisslén, L., Eakins, A., Gordillo, C., Bergdahl, J., & Tollmar, K. Adversarial reinforcement learning for procedural content generation. Arxiv, 2021 [Paper]
arch:adversarial
env:unity
- Dennis, M., Jaques, N., Vinitsky, E., Bayen, A., Russell, S., Critch, A., & Levine, S. Emergent complexity and zero-shot transfer via unsupervised environment design. Arxiv, 2020 [Paper]
arch:adversarial
env:minigrid
- Shu, T., Liu, J., & Yannakakis, G. N Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros Study . Arxiv, 2021. [Paper]
arch:adversarial
env:smb
- Awiszus, M., Schubert, F., & Rosenhahn, B. World-GAN: a Generative Model for Minecraft Worlds. Arxiv, 2021 [Paper]
env:minecraft
- Delarosa, O., Dong, H., Ruan, M., Khalifa, A., & Togelius, J. (2020). Mixed-Initiative Level Design with RL Brush. ArXiv, 2020 [Paper]
env:pcgrl