Controllable Level Blending between Games using Variational Autoencoders

• URL: http://arxiv.org/abs/2002.11869v1
• Date: Thu, 27 Feb 2020 01:38:35 GMT
• Title: Controllable Level Blending between Games using Variational Autoencoders
• Authors: Anurag Sarkar, Zhihan Yang, Seth Cooper
• Abstract summary: We train a VAE on level data from Super Mario Bros. and Kid Icarus, enabling it to capture the latent space spanning both games. We then use this space to generate level segments that combine properties of levels from both games. We argue that these affordances make the VAE-based approach especially suitable for co-creative level design.
• Score: 6.217860411034386
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Previous work explored blending levels from existing games to create levels for a new game that mixes properties of the original games. In this paper, we use Variational Autoencoders (VAEs) for improving upon such techniques. VAEs are artificial neural networks that learn and use latent representations of datasets to generate novel outputs. We train a VAE on level data from Super Mario Bros. and Kid Icarus, enabling it to capture the latent space spanning both games. We then use this space to generate level segments that combine properties of levels from both games. Moreover, by applying evolutionary search in the latent space, we evolve level segments satisfying specific constraints. We argue that these affordances make the VAE-based approach especially suitable for co-creative level design and compare its performance with similar generative models like the GAN and the VAE-GAN.

Related papers

• Unbounded: A Generative Infinite Game of Character Life Simulation [68.37260000219479]
  We introduce the concept of a generative infinite game, a video game that transcends the traditional boundaries of finite, hard-coded systems by using generative models. We leverage recent advances in generative AI to create Unbounded: a game of character life simulation that is fully encapsulated in generative models.
  arXiv  Detail & Related papers  (2024-10-24T17:59:31Z)
• Latent Combinational Game Design [4.8951183832371]
  We present an approach for generating playable games that blend a given set of games in a desired combination using deep generative latent variable models. Results show that these approaches can generate playable games that blend the input games in specified combinations.
  arXiv  Detail & Related papers  (2022-06-28T17:54:17Z)
• Multi-Game Decision Transformers [49.257185338595434]
  We show that a single transformer-based model can play a suite of up to 46 Atari games simultaneously at close-to-human performance. We compare several approaches in this multi-game setting, such as online and offline RL methods and behavioral cloning. We find that our Multi-Game Decision Transformer models offer the best scalability and performance.
  arXiv  Detail & Related papers  (2022-05-30T16:55:38Z)
• Unsupervised Motion Representation Learning with Capsule Autoencoders [54.81628825371412]
  Motion Capsule Autoencoder (MCAE) models motion in a two-level hierarchy. MCAE is evaluated on a novel Trajectory20 motion dataset and various real-world skeleton-based human action datasets.
  arXiv  Detail & Related papers  (2021-10-01T16:52:03Z)
• Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling [79.15521784128102]
  We introduce a novel neural network for building image generators (decoders) and apply it to variational autoencoders (VAEs) In our spatial dependency networks (SDNs), feature maps at each level of a deep neural net are computed in a spatially coherent way. We show that augmenting the decoder of a hierarchical VAE by spatial dependency layers considerably improves density estimation.
  arXiv  Detail & Related papers  (2021-03-16T07:01:08Z)
• Generating and Blending Game Levels via Quality-Diversity in the Latent Space of a Variational Autoencoder [7.919213739992465]
  We present a level generation and game blending approach that combines the use of VAEs and QD algorithms. Specifically, we train VAEs on game levels and then run the MAP-Elites QD algorithm using the learned latent space of the VAE as the search space.
  arXiv  Detail & Related papers  (2021-02-24T18:44:23Z)
• Deep Policy Networks for NPC Behaviors that Adapt to Changing Design Parameters in Roguelike Games [137.86426963572214]
  Turn-based strategy games like Roguelikes, for example, present unique challenges to Deep Reinforcement Learning (DRL) We propose two network architectures to better handle complex categorical state spaces and to mitigate the need for retraining forced by design decisions.
  arXiv  Detail & Related papers  (2020-12-07T08:47:25Z)
• Conditional Level Generation and Game Blending [6.217860411034386]
  We build on prior research by exploring the level design affordances and applications enabled by conditional VAEs (CVAEs) CVAEs augment VAEs by allowing them to be trained using labeled data, thus enabling outputs to be generated conditioned on some input. We show that such models can assist in level design by generating levels with desired level elements and patterns as well as producing blended levels with desired combinations of games.
  arXiv  Detail & Related papers  (2020-10-13T00:28:20Z)
• Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks [75.69896269357005]
  Mixup is the latest data augmentation technique that linearly interpolates input examples and the corresponding labels. In this paper, we explore how to apply mixup to natural language processing tasks. We incorporate mixup to transformer-based pre-trained architecture, named "mixup-transformer", for a wide range of NLP tasks.
  arXiv  Detail & Related papers  (2020-10-05T23:37:30Z)
• Game Level Clustering and Generation using Gaussian Mixture VAEs [6.217860411034386]
  Variational autoencoders (VAEs) have been shown to be able to generate game levels but require manual exploration of the learned latent space to generate outputs with desired attributes. In this paper, we apply a variant of the VAE which imposes a mixture of Gaussians (GM) on the latent space, unlike regular VAEs which impose a unimodal Gaussian. This allows GMVAEs to cluster levels in an unsupervised manner using the components of the GM and then generate new levels using the learned components.
  arXiv  Detail & Related papers  (2020-08-22T15:07:30Z)
• Illuminating Mario Scenes in the Latent Space of a Generative Adversarial Network [11.055580854275474]
  We show how designers may specify gameplay measures to our system and extract high-quality (playable) levels with a diverse range of level mechanics. An online user study shows how the different mechanics of the automatically generated levels affect subjective ratings of their perceived difficulty and appearance.
  arXiv  Detail & Related papers  (2020-07-11T03:38:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.