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Improving generalization ability in a puzzle game using reinforcement learning

Nowadays machine learning has attracted much attention. In order to apply it to various problems without relearning, its generalization ability is needed. Geometry Friends is a puzzle game where a player has to collect all targets in a two-dimensional world, and it is used in some artificial intel­ligence competitions. Although sufficient generalization ability is needed to apply the machine learning to this game, such machine learning methods are not proposed yet. In this paper, we propose a method based on reinforcement learning in which the generalization ability is improved for Geometry Friends.