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Deep learning for video game playing

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Deep learning for video game playing. / Justesen, Niels; Bontrager, Philip; Togelius, Julian; Risi, Sebastian.

In: IEEE Transactions on Games, 2019.

Research output: Journal Article or Conference Article in JournalJournal articleResearchpeer-review

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Bibtex

@article{23cd9f7a3dba4fa388da76dcbf6e7057,
title = "Deep learning for video game playing",
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.",
author = "Niels Justesen and Philip Bontrager and Julian Togelius and Sebastian Risi",
year = "2019",
doi = "10.1109/TG.2019.2896986",
language = "English",
journal = "IEEE Transactions on Games",
publisher = "IEEE",

}

RIS

TY - JOUR

T1 - Deep learning for video game playing

AU - Justesen, Niels

AU - Bontrager, Philip

AU - Togelius, Julian

AU - Risi, Sebastian

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

U2 - 10.1109/TG.2019.2896986

DO - 10.1109/TG.2019.2896986

M3 - Journal article

JO - IEEE Transactions on Games

JF - IEEE Transactions on Games

ER -

ID: 84782842