mlsanityData to 14 Oct 2026

Evolutionary Machine Learning and Games

arXiv 2311.16172 ↗First posted 20 Nov 2023cs.NE cs.AI cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Evolutionary machine learning (EML) has been applied to games in multiple ways, and for multiple different purposes. Importantly, AI research in games is not only about playing games; it is also about generating game content, modeling players, and many other applications. Many of these applications pose interesting problems for EML. We will structure this chapter on EML for games based on whether evolution is used to augment machine learning (ML) or ML is used to augment evolution. For completeness, we also briefly discuss the usage of ML and evolution separately in games.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 47 of its 115 references that are in the map. First posted on arXiv 20 Nov 2023.

Builds on

47 of 115 references.

References · 47

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  2. 2022Generative Adversarial Networkscited by 5,627
  3. 2018World Modelscited by 1,513
  4. 2017Evolution Strategies as a Scalable Alternative to Reinforcement Learningcited by 574
  5. 2022Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videoscited by 345
  6. 2020Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Designcited by 275
  7. 2020First return, then explorecited by 266
  8. 2019MineRL: A Large-Scale Dataset of Minecraft Demonstrationscited by 215
  9. 2019Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutionscited by 192
  10. 2021Open-Ended Learning Leads to Generally Capable Agentscited by 182
  11. 2016ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learningcited by 179
  12. 2022Evolving Curricula with Regret-Based Environment Designcited by 173
35 more
  1. 2023Human-Timescale Adaptation in an Open-Ended Task Spacecited by 138
  2. 2017Procedural Content Generation via Machine Learning (PCGML)cited by 137
  3. 2022Evolution through Large Modelscited by 134
  4. 2020PCGRL: Procedural Content Generation via Reinforcement Learningcited by 90
  5. 2019Covariance Matrix Adaptation for the Rapid Illumination of Behavior Spacecited by 78
  6. 2018Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Networkcited by 76
  7. 2020Illuminating Mario Scenes in the Latent Space of a Generative Adversarial Networkcited by 52
  8. 2016Super Mario as a String: Platformer Level Generation Via LSTMscited by 40
  9. 2022Deep Surrogate Assisted Generation of Environmentscited by 40
  10. 2021Illuminating Diverse Neural Cellular Automata for Level Generationcited by 35
  11. 2017Deep Learning for Video Game Playingcited by 30
  12. 2018Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Ataricited by 28
  13. 2021Deep Surrogate Assisted MAP-Elites for Automated Hearthstone Deckbuildingcited by 26
  14. 2020TOAD-GAN: Coherent Style Level Generation from a Single Examplecited by 22
  15. 2014Neuroevolution in Games: State of the Art and Open Challengescited by 15
  16. 2018Deep Interactive Evolutioncited by 14
  17. 2020Co-generation of game levels and game-playing agentscited by 14
  18. 2021Transforming Exploratory Creativity with DeLeNoXcited by 13
  19. 2018Quality Diversity Through Surprisecited by 12
  20. 2018Playing Atari with Six Neuronscited by 11
  21. 2019Intentional Computational Level Designcited by 9
  22. 2022Mutation Models: Learning to Generate Levels by Imitating Evolutioncited by 9
  23. 2022Open-Ended Evolution for Minecraft Building Generationcited by 9
  24. 2017Autoencoder-augmented Neuroevolution for Visual Doom Playingcited by 8
  25. 2022Generative Personas That Behave and Experience Like Humanscited by 7
  26. 2021SuSketch: Surrogate Models of Gameplay as a Design Assistantcited by 6
  27. 2021Level Generation for Angry Birds with Sequential VAE and Latent Variable Evolutioncited by 6
  28. 2016Demonstrating the Feasibility of Automatic Game Balancingcited by 5
  29. 2019CG-GAN: An Interactive Evolutionary GAN-based Approach for Facial Composite Generation)cited by 5
  30. 2022Preference-Learning Emitters for Mixed-Initiative Quality-Diversity Algorithmscited by 5
  31. 2021Hybrid Encoding For Generating Large Scale Game Level Patterns With Local Variationscited by 4
  32. 2020Evolutionary Planning in Latent Spacecited by 3
  33. 2019Learning Local Forward Models on Unforgiving Gamescited by 1
  34. 2020Rolling Horizon NEAT for General Video Game Playingcited by 1
  35. 2021Playing Against the Board: Rolling Horizon Evolutionary Algorithms Against Pandemiccited by 1

Built on by

Not cited yet.