Deep Learning for Video Game Playing

TL;DR

This paper reviews recent deep learning methods applied to various video game genres, discussing their challenges and open problems like generalization, large decision spaces, and sparse rewards.

Contribution

It provides a comprehensive overview of deep learning applications in video games and highlights key open challenges for future research.

Findings

Deep learning has been successfully applied to multiple game genres.

Open challenges include general game playing and handling large decision spaces.

Sparse rewards remain a significant obstacle in game AI development.

Abstract

In this article, we review recent Deep Learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games. We analyze the unique requirements that different game genres pose to a deep learning system and highlight important open challenges in the context of applying these machine learning methods to video games, such as general game playing, dealing with extremely large decision spaces and sparse rewards.