Level Generation for Angry Birds with Sequential VAE and Latent Variable Evolution

04/13/2021
by   Takumi Tanabe, et al.
0

Video game level generation based on machine learning (ML), in particular, deep generative models, has attracted attention as a technique to automate level generation. However, applications of existing ML-based level generations are mostly limited to tile-based level representation. When ML techniques are applied to game domains with non-tile-based level representation, such as Angry Birds, where objects in a level are specified by real-valued parameters, ML often fails to generate playable levels. In this study, we develop a deep-generative-model-based level generation for the game domain of Angry Birds. To overcome these drawbacks, we propose a sequential encoding of a level and process it as text data, whereas existing approaches employ a tile-based encoding and process it as an image. Experiments show that the proposed level generator drastically improves the stability and diversity of generated levels compared with existing approaches. We apply latent variable evolution with the proposed generator to control the feature of a generated level computed through an AI agent's play, while keeping the level stable and natural.

READ FULL TEXT

page 3

page 6

page 7

page 8

page 11

page 12

research
07/25/2020

Towards Game Design via Creative Machine Learning (GDCML)

In recent years, machine learning (ML) systems have been increasingly ap...
research
07/17/2020

Sequential Segment-based Level Generation and Blending using Variational Autoencoders

Existing methods of level generation using latent variable models such a...
research
09/20/2020

Factorized Deep Generative Models for Trajectory Generation with Spatiotemporal-Validity Constraints

Trajectory data generation is an important domain that characterizes the...
research
07/11/2020

Illuminating Mario Scenes in the Latent Space of a Generative Adversarial Network

Recent developments in machine learning techniques have allowed automati...
research
06/28/2022

Latent Combinational Game Design

We present an approach for generating playable games that blend a given ...
research
03/08/2018

A Deep Generative Model for Disentangled Representations of Sequential Data

We present a VAE architecture for encoding and generating high dimension...
research
05/02/2019

Coordination and Trajectory Prediction for Vehicle Interactions via Bayesian Generative Modeling

Coordination recognition and subtle pattern prediction of future traject...

Please sign up or login with your details

Forgot password? Click here to reset