PCG-papers

KO | EN

gitlite — search

#python #react #go #golang #machine-learning #rust #docker #macos #java #kubernetes #awesome-list #mcp #openai #open-source

(★ 9)

Procedural Content Generation (PCG) papers summarization for newbies :)

PCG-papers 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# PCG-papers
In this article, we summarize the PCG (Procedural Content Generation) papers for game research.

## Survey
- Smith, G. Understanding procedural content generation: a design-centric analysis of the role of PCG in games. CHI, 2014 [[Paper]](http://www.cas.mcmaster.ca/~carette/CAS761/Understanding%20PCG-A%20Design%20Centric%20Analysis.pdf)
- 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]](https://idp.springer.com/authorize/casa?redirect_uri=https://link.springer.com/article/10.1007/s00521-020-05383-8&casa_token=XMAprJ6NFdEAAAAA:cpsxxGW4tpAUPNnCbiLMe2hHKTF0lmb_I0Wf5xGIM7Hv9sgJyyvwP5PfUUjt33XltJtzkXBMS7XEAbvfIw)

## RL-based
- Khalifa, A., Bontrager, P., Earle, S., & Togelius, J. Pcgrl: Procedural content generation via reinforcement learning. AIIDE, 2020 [[Paper]](https://ojs.aaai.org/index.php/AIIDE/article/download/7416/7341) [[Code]](https://github.com/amidos2006/gym-pcgrl) `env:pcgrl`
- Earle, S., Edwards, M., Khalifa, A., Bontrager, P., & Togelius, J. Learning Controllable Content Generators. Arxiv, 2021 [[Paper]](https://arxiv.org/pdf/2105.02993) `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]](https://arxiv.org/pdf/2103.04847) `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]](https://arxiv.org/pdf/2012.02096) `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]](https://arxiv.org/pdf/2106.15877) `arch:adversarial` `env:smb`

## GAN-based
- Awiszus, M., Schubert, F., & Rosenhahn, B. World-GAN: a Generative Model for Minecraft Worlds. Arxiv, 2021 [[Paper]](https://arxiv.org/pdf/2106.10155) `env:minecraft`

## Utility
- Delarosa, O., Dong, H., Ruan, M., Khalifa, A., & Togelius, J. (2020). Mixed-Initiative Level Design with RL Brush. ArXiv, 2020 [[Paper]](https://arxiv.org/pdf/2008.02778) `env:pcgrl`

PCG-papers

PCG-papers 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

PCG-papers

PCG-papers 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README