Evaluating Game Difficulty in Tetris Block Puzzle
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914409769598976 |
|---|---|
| author | Wang, Chun-Jui Guo, Jian-Ting Guei, Hung Shih, Chung-Chin Wu, Ti-Rong Wu, I-Chen |
| author_facet | Wang, Chun-Jui Guo, Jian-Ting Guei, Hung Shih, Chung-Chin Wu, Ti-Rong Wu, I-Chen |
| contents | Tetris Block Puzzle is a single player stochastic puzzle in which a player places blocks on an 8 x 8 grid to complete lines; its popular variants have amassed tens of millions of downloads. Despite this reach, there is little principled assessment of which rule sets are more difficult. Inspired by prior work that uses AlphaZero as a strong evaluator for chess variants, we study difficulty in this domain using Stochastic Gumbel AlphaZero (SGAZ), a budget-aware planning agent for stochastic environments. We evaluate rule changes including holding block h, preview holding block p, and additional Tetris block variants using metrics such as training reward and convergence iterations. Empirically, increasing h and p reduces difficulty (higher reward and faster convergence), while adding more Tetris block variants increases difficulty, with the T-pentomino producing the largest slowdown. Through analysis, SGAZ delivers strong play under small simulation budgets, enabling efficient, reproducible comparisons across rule sets and providing a reference for future design in stochastic puzzle games. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_18994 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Evaluating Game Difficulty in Tetris Block Puzzle Wang, Chun-Jui Guo, Jian-Ting Guei, Hung Shih, Chung-Chin Wu, Ti-Rong Wu, I-Chen Artificial Intelligence Machine Learning Tetris Block Puzzle is a single player stochastic puzzle in which a player places blocks on an 8 x 8 grid to complete lines; its popular variants have amassed tens of millions of downloads. Despite this reach, there is little principled assessment of which rule sets are more difficult. Inspired by prior work that uses AlphaZero as a strong evaluator for chess variants, we study difficulty in this domain using Stochastic Gumbel AlphaZero (SGAZ), a budget-aware planning agent for stochastic environments. We evaluate rule changes including holding block h, preview holding block p, and additional Tetris block variants using metrics such as training reward and convergence iterations. Empirically, increasing h and p reduces difficulty (higher reward and faster convergence), while adding more Tetris block variants increases difficulty, with the T-pentomino producing the largest slowdown. Through analysis, SGAZ delivers strong play under small simulation budgets, enabling efficient, reproducible comparisons across rule sets and providing a reference for future design in stochastic puzzle games. |
| title | Evaluating Game Difficulty in Tetris Block Puzzle |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2603.18994 |