DOOM Level Generation using Generative Adversarial Networks

[1804.09154] DOOM Level Generation using Generative Adversarial Networks Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:1804.09154 (cs) [Submitted on 24 Apr 2018] Title: DOOM Level Generation using Generative Adversarial Networks Authors: Edoardo Giacomello , Pier Luca Lanzi , Daniele Loiacono View a PDF of the paper titled DOOM Level Generation using Generative Adversarial Networks, by Edoardo Giacomello and Pier Luca Lanzi and Daniele Loiacono View PDF HTML (experimental) Abstract: We applied Generative Adversarial Networks (GANs) to learn a model of DOOM levels from human-designed content. Initially, we analysed the levels and extracted several topological features. Then, for each level, we extracted a set of images identifying the occupied area, the height map, the walls, and the position of game objects. We trained two GANs: one using plain level images, one using both the images and some of the features extracted during the preliminary analysis. We used the two networks to generate new levels and compared the results to assess whether the network trained using also the topological features could generate levels more similar to human-designed ones. Our results show that GANs can capture intrinsic structure of DOOM levels and appears to be a promising approach to level generation in first person shooter games. Subjects: Machine Learning (cs.LG) ; Human-Computer Interaction (cs.HC); Machine Learning (stat.ML) Cite as: arXiv:1804.09154 [cs.LG] (or arXiv:1804.09154v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.1804.09154 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1109/GEM.2018.8516539 Focus to learn more DOI(s) linking to related resources Submission history From: Pier Luca Lanzi [ view email ] [v1] Tue, 24 Apr 2018 17:20:52 UTC (1,066 KB) Full-text links: Access Paper: View a PDF of the paper titled DOOM Level Generation using Generative Adversarial Networks, by Edoardo Giacomello and Pier Luca Lanzi and Daniele Loiacono View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2018-04 Change to browse by: cs cs.HC stat stat.ML References & Citations NASA ADS Google Scholar Semantic Scholar 1 blog link ( what is this? ) DBLP - CS Bibliography listing | bibtex Edoardo Giacomello Pier Luca Lanzi Daniele Loiacono export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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