DOOM Level Generation using Generative Adversarial Networks
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2018
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| _version_ | 1866908965252628480 |
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| author | Giacomello, Edoardo Lanzi, Pier Luca Loiacono, Daniele |
| author_facet | Giacomello, Edoardo Lanzi, Pier Luca Loiacono, Daniele |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1804_09154 |
| institution | arXiv |
| publishDate | 2018 |
| record_format | arxiv |
| spellingShingle | DOOM Level Generation using Generative Adversarial Networks Giacomello, Edoardo Lanzi, Pier Luca Loiacono, Daniele Machine Learning Human-Computer Interaction 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. |
| title | DOOM Level Generation using Generative Adversarial Networks |
| topic | Machine Learning Human-Computer Interaction |
| url | https://arxiv.org/abs/1804.09154 |