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Deep Learning for Video Game Playing

Niels Justesen, Philip Bontrager, Julian Togelius, Sebastian Risi

In this paper 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 or 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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