Abstract

As part of the game development cycle, game designers undergo a time-consuming and resource-intensive process of iteratively playtesting, re-designing, and fine-tuning their game before it is released. Using reinforcement learning, RealmAI eases this process by training bots to play games, during which, gameplay data are generated, recorded, and analyzed by the tool. Game designers can then view the analyzed results to make data-driven decisions, without the need of a large playtesting team or other post-release analytic tools to provide these insights.

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