mlsanityData to 7 Oct 2026

Deep Learning for Procedural Content Generation

arXiv 2010.04548 ↗First posted 9 Oct 2020cs.AI cs.LGcited by 73

Not on the map. Why?

Abstract (arXiv)

Procedural content generation in video games has a long history. Existing procedural content generation methods, such as search-based, solver-based, rule-based and grammar-based methods have been applied to various content types such as levels, maps, character models, and textures. A research field centered on content generation in games has existed for more than a decade. More recently, deep learning has powered a remarkable range of inventions in content production, which are applicable to games. While some cutting-edge deep learning methods are applied on their own, others are applied in combination with more traditional methods, or in an interactive setting. This article surveys the various deep learning methods that have been applied to generate game content directly or indirectly, discusses deep learning methods that could be used for content generation purposes but are rarely used today, and envisages some limitations and potential future directions of deep learning for procedural content generation.

Key facts. Cited by 73 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Oct 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 11; 2022: 10; 2023: 16; 2024: 18; 2025: 13; 2026: 5.

73 papers.

Sort:

By area:

  1. 2022Evolving Curricula with Regret-Based Environment Designcited by 166
  2. 2023Large Language Models in Law: A Surveycited by 128
  3. 2023Level Generation Through Large Language Modelscited by 68
  4. 2023MarioGPT: Open-Ended Text2Level Generation through Large Language Modelscited by 63
  5. 2024EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agentscited by 41
  6. 2021Learning Controllable Content Generatorscited by 40
  7. 2022Deep Surrogate Assisted Generation of Environmentscited by 40
  8. 2021Copyright in Generative Deep Learningcited by 29
  9. 2024A Comprehensive Survey on 3D Content Generationcited by 28
  10. 2021Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros Studycited by 25
  11. 2024Game Generation via Large Language Modelscited by 24
  12. 2023Are Emily and Greg Still More Employable than Lakisha and Jamal? Investigating Algorithmic Hiring Bias in the Era of ChatGPTcited by 23
38 more
  1. 2024Procedural Content Generation in Games: A Survey with Insights on Emerging LLM Integrationcited by 22
  2. 2021Creativity and Machine Learning: A Surveycited by 21
  3. 2023Practical PCG Through Large Language Modelscited by 21
  4. 2024Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent Circumventioncited by 21
  5. 2024Word2World: Generating Stories and Worlds through Large Language Modelscited by 18
  6. 2024DreamCraft: Text-Guided Generation of Functional 3D Environments in Minecraftcited by 17
  7. 2023Games for Artificial Intelligence Research: A Review and Perspectivescited by 14
  8. 2021Contrastive Learning of Generalized Game Representationscited by 11
  9. 2023Affective Game Computing: A Surveycited by 11
  10. 2022DareFightingICE Competition: A Fighting Game Sound Design and AI Competitioncited by 10
  11. 2022Mutation Models: Learning to Generate Levels by Imitating Evolutioncited by 9
  12. 2025GVGAI-LLM: Evaluating Large Language Model Agents with Infinite Gamescited by 8
  13. 2024Grammar-based Game Description Generation using Large Language Modelscited by 7
  14. 2024PCGRL+: Scaling, Control and Generalization in Reinforcement Learning Level Generatorscited by 7
  15. 2025AnimeGamer: Infinite Anime Life Simulation with Next Game State Predictioncited by 7
  16. 2021SuSketch: Surrogate Models of Gameplay as a Design Assistantcited by 6
  17. 2021Level Generation for Angry Birds with Sequential VAE and Latent Variable Evolutioncited by 6
  18. 2022Reinforcement Learning in Practice: Opportunities and Challengescited by 5
  19. 2024Measuring Diversity of Game Scenarioscited by 5
  20. 2025LatticeWorld: A Multimodal Large Language Model-Empowered Framework for Interactive Complex World Generationcited by 5
  21. 2023Hierarchically Composing Level Generators for the Creation of Complex Structurescited by 4
  22. 2023Balancing of competitive two-player Game Levels with Reinforcement Learningcited by 4
  23. 2023Point Cloud Part Editing: Segmentation, Generation, Assembly, and Selectioncited by 4
  24. 2024G-PCGRL: Procedural Graph Data Generation via Reinforcement Learningcited by 4
  25. 2021Using Multiple Generative Adversarial Networks to Build Better-Connected Levels for Mega Mancited by 3
  26. 2023The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directionscited by 3
  27. 20243D Building Generation in Minecraft via Large Language Modelscited by 3
  28. 2025PCGRLLM: Large Language Model-Driven Reward Design for Procedural Content Generation Reinforcement Learningcited by 3
  29. 2025Word2Minecraft: Generating 3D Game Levels through Large Language Modelscited by 3
  30. 2025DANTE-AD: Dual-Vision Attention Network for Long-Term Audio Descriptioncited by 3
  31. 2021Keiki: Towards Realistic Danmaku Generation via Sequential GANscited by 2
  32. 2022Generating Game Levels of Diverse Behaviour Engagementcited by 2
  33. 2022Online Game Level Generation from Musiccited by 2
  34. 2022TreeSketchNet: From Sketch To 3D Tree Parameters Generationcited by 2
  35. 2023PlotMap: Automated Layout Design for Building Game Worldscited by 2
  36. 2024Autoverse: An Evolvable Game Language for Learning Robust Embodied Agentscited by 2
  37. 2022State Space Closure: Revisiting Endless Online Level Generation via Reinforcement Learningcited by 1
  38. 2023Controllable Path of Destructioncited by 1

50 most cited of 73 All 73 (JSON)