On-Device, Diverse, Difficulty-Driven Level Generation for Match 3D Puzzles via Reinforcement Learning

Three-dimensional match-3 puzzle games (Match 3D) are a sub-genre of tile-matching puzzles in which players identify and remove sets of three identical 3D objects. Sustaining long-term player engagement in Match 3D games requires diverse and appropriately difficult levels, yet manual design is resource-intensive. In this paper, we present an on-device level generation model that produces a virtually infinite number of levels with minimal designer intervention. By applying reinforcement learning (RL), our model generates levels designed to meet specified difficulty targets through a reward function based on completion time estimates derived from human play data. We evaluate our system by assessing the accuracy of difficulty matching, the diversity of generated content, and the run-time performance on smartphones via ONNX and Unity Barracuda. Experimental results confirm that the RL approach generates diverse, challenging, and engaging levels, thereby offering a sustainable solution for Match 3D puzzle game design. Furthermore, our proposed system has been seamlessly integrated into a commercial Match 3D game application, providing users with a real-world gameplay experience. To the best of our knowledge, this represents the first successful deployment of PCGRL for on-device, real-time level generation in a commercial mobile puzzle game.