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arXiv · 2012.01148

Applied Machine Learning for Games: A Graduate School Course

Yilei Zeng·Aayush Shah·Jameson Thai·Michael Zyda
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Abstract

The game industry is moving into an era where old-style game engines are being replaced by re-engineered systems with embedded machine learning technologies for the operation, analysis and understanding of game play. In this paper, we describe our machine learning course designed for graduate students interested in applying recent advances of deep learning and reinforcement learning towards gaming. This course serves as a bridge to foster interdisciplinary collaboration among graduate schools and does not require prior experience designing or building games. Graduate students enrolled in this course apply different fields of machine learning techniques such as computer vision, natural language processing, computer graphics, human computer interaction, robotics and data analysis to solve open challenges in gaming. Student projects cover use-cases such as training AI-bots in gaming benchmark environments and competitions, understanding human decision patterns in gaming, and creating intelligent non-playable characters or environments to foster engaging gameplay. Projects demos can help students open doors for an industry career, aim for publications, or lay the foundations of a future product. Our students gained hands-on experience in applying state of the art machine learning techniques to solve real-life problems in gaming.

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Yilei Zeng, Aayush Shah, Jameson Thai, Michael Zyda. 2020-11-30. Applied Machine Learning for Games: A Graduate School Course. https://arxiv.org/abs/2012.01148

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