mlsanityData to 14 Oct 2026

Ensemble Learning For Mega Man Level Generation

arXiv 2107.12524 ↗First posted 27 Jul 2021cs.LGcited by 1

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Abstract (arXiv)

Procedural content generation via machine learning (PCGML) is the process of procedurally generating game content using models trained on existing game content. PCGML methods can struggle to capture the true variance present in underlying data with a single model. In this paper, we investigated the use of ensembles of Markov chains for procedurally generating Mega Man levels. We conduct an initial investigation of our approach and evaluate it on measures of playability and stylistic similarity in comparison to a non-ensemble, existing Markov chain approach.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 37 references that are in the map. First posted on arXiv 27 Jul 2021.

Builds on

17 of 37 references.

References · 17

  1. 2017Procedural Content Generation via Machine Learning (PCGML)cited by 137
  2. 2020PCGRL: Procedural Content Generation via Reinforcement Learningcited by 90
  3. 2018Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Networkcited by 76
  4. 2016The VGLC: The Video Game Level Corpuscited by 41
  5. 2016Super Mario as a String: Platformer Level Generation Via LSTMscited by 40
  6. 2019Bootstrapping Conditional GANs for Video Game Level Generationcited by 35
  7. 2021Generating and Blending Game Levels via Quality-Diversity in the Latent Space of a Variational Autoencodercited by 27
  8. 2020TOAD-GAN: Coherent Style Level Generation from a Single Examplecited by 22
  9. 2020Conditional Level Generation and Game Blendingcited by 17
  10. 2018Automated Game Design via Conceptual Expansioncited by 15
  11. 2020Sequential Segment-based Level Generation and Blending using Variational Autoencoderscited by 14
  12. 2020Exploring Level Blending across Platformers via Paths and Affordancescited by 14
5 more
  1. 2020Game Level Clustering and Generation using Gaussian Mixture VAEscited by 9
  2. 2018Explainable PCGML via Game Design Patternscited by 7
  3. 2016Toward Game Level Generation from Gameplay Videoscited by 6
  4. 2017Automatic Mapping of NES Games with Mappycited by 3
  5. 2021Using Multiple Generative Adversarial Networks to Build Better-Connected Levels for Mega Mancited by 3

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Cited by year

Citing papers per year: 2022: 1.

1 paper.

  1. 2022On Linking Level Segmentscited by 1