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Deep Learning for Video Game Playing (arxiv.org)
2 points by lainon on Aug 29, 2017 | hide | past | pdf | discuss on HN

In plain words: This review surveys how deep learning systems learn to play games from arcade titles to shooters and strategy games, and what each genre demands. The biggest unsolved problems are playing new games without retraining, choosing among enormous move sets, and learning from rare wins.

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.

Niels Justesen, Philip Bontrager, Julian Togelius, Sebastian Risi
arXiv:1708.07902 · cs.AI · submitted Aug 25, 2017 · updated Feb 18, 2019
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Also discussed: Feb 2019 (2 points, 0 comments)