General Video Game AI: a Multi-Track Framework for Evaluating Agents, Games and Content Generation Algorithms

TL;DR

This paper reviews the GVGAI framework, a platform for evaluating AI agents across multiple video games, highlighting its structure, research applications, and future development plans.

Contribution

It provides a comprehensive overview of the GVGAI framework, including its design, various research tracks, and its impact on AI research and education.

Findings

GVGAI supports diverse AI research tracks.

The framework has been widely adopted in research and education.

Future improvements are planned for the framework.

Abstract

General Video Game Playing (GVGP) aims at designing an agent that is capable of playing multiple video games with no human intervention. In 2014, The General Video Game AI (GVGAI) competition framework was created and released with the purpose of providing researchers a common open-source and easy to use platform for testing their AI methods with potentially infinity of games created using Video Game Description Language (VGDL). The framework has been expanded into several tracks during the last few years to meet the demand of different research directions. The agents are required either to play multiple unknown games with or without access to game simulations, or to design new game levels or rules. This survey paper presents the VGDL, the GVGAI framework, existing tracks, and reviews the wide use of GVGAI framework in research, education and competitions five years after its birth. A future plan of framework improvements is also described.