Evaluating Game Difficulty in Tetris Block Puzzle
This provides a principled, reproducible method for game designers to assess difficulty in stochastic puzzle games, though it is incremental as it applies an existing agent framework to a new domain.
The researchers tackled the problem of evaluating game difficulty in Tetris Block Puzzle variants by using Stochastic Gumbel AlphaZero (SGAZ) as a planning agent to assess rule changes. They found that increasing holding blocks (h) and preview blocks (p) reduced difficulty (higher reward and faster convergence), while adding more block variants increased difficulty, with the T-pentomino causing the largest slowdown.
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.