Wenhui Ruan

Minesweeper is both an intellectual challenge for human players and an effective testbed for intelligent algorithms. For the standard Minesweeper task on a 9 × 9 grid with 10 mines, a convolutional neural network (CNN)-based supervised learning approach, together with optimized network architecture, state encoding, and dynamic learning rate scheduling, achieves a test win rate of 85.16% (SD = 0.36 percentage points), exceeding the baseline Single-Point (SP) algorithm by more than 10 percentage points. The combined use of dropout and data augmentation achieves a higher test win rate than either technique alone under the evaluated settings. Building on this model, we propose an easy-to-hard staged curriculum learning (SCL) strategy to address the increasing task difficulty caused by higher mine density and the difficulty of training models from scratch. By progressively increasing the number of mines and transferring model parameters from the preceding training stage, the model can inherit previously acquired knowledge and gradually adapt to more complex tasks. Representative SCL models achieved test win rates of 55.85%, 20.38%, and 4.00% on the 14-, 18-, and 22-mine tasks, respectively. In the three-seed ablation study on the 18-mine task using validation-based checkpoint selection, SCL achieved the highest mean test win rate of 21.65%, compared with 20.85% for mixed-density adaptation, 17.08% for direct transfer, and 15.59% for training from scratch. SCL achieved numerically higher test win rates than mixed-density adaptation across all three seeds, although the difference was not statistically significant. However, a constraint satisfaction problem (CSP) solver achieves a higher win rate of 47.17% on the same task, highlighting the advantage of explicit constraint-based reasoning in absolute solving performance. Further analysis of clue-value distributions shows that the frequencies of local patterns change with mine density, providing a structural motivation for the easy-to-hard curriculum. These findings indicate that staged curriculum learning can facilitate knowledge transfer across increasingly difficult Minesweeper tasks, although reliable solving of high-density configurations remains challenging.