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

TL;DR

This paper introduces a novel sequential VAE approach for generating playable and diverse Angry Birds levels by encoding levels as text data, overcoming limitations of tile-based methods.

Contribution

It proposes a sequential encoding method and latent variable evolution for stable, natural level generation in non-tile-based game domains like Angry Birds.

Findings

Significantly improved stability of generated levels

Enhanced diversity of generated levels

Effective control of level features via latent variables

Abstract

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.