Combining Evolutionary Search with Behaviour Cloning for Procedurally Generated Content

نویسندگان

چکیده

In this work, we consider the problem of procedural content generation for video game levels. Prior approaches have relied on evolutionary search (ES) methods capable gener- ating diverse levels, but procedure is slow, which problematic in real-time settings. Reinforcement learning (RL) has also been proposed to tackle same problem, and while level fast, training time can be prohibitively expensive. We propose a framework that combines best ES RL. particular, our approach first uses generate sequence levels evolved over time, then behaviour cloning distil these into policy, queried produce new quickly. apply maze Super Mario Bros, with results indicating does fact decrease required generation, especially when an increasing number valid are required.

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ژورنال

عنوان ژورنال: EPiC series in computing

سال: 2022

ISSN: ['2398-7340']

DOI: https://doi.org/10.29007/qpkt