Deep learning for video game playing
- Niels Justesen,
- Philip Bontrager,
- Julian Togelius,
- New York University
Research Output:
Journal Article or Conference Article in Journal
Journal article
Peer-reviewOpen access
Publication Information
Output type
Research Output:
Journal Article or Conference Article in Journal
Journal article
Peer-reviewOriginal language
EnglishJournal (Volume, Issue Number)
IEEE Transactions on GamesPublication milestones
- Published - 2019
Publication status
Published - 2019
Publication IDs
- Scopus: 85089102332
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.
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Citations
175
Mentions
4
Captures
669
Access to documents
Accepted author manuscript, 1.78 MB
