Achieving optimal balance in games is essential to their success, yet reliant on extensive manual work and playtesting. To facilitate this process, the Procedural Content Generation via Reinforcement Learning (PCGRL) framework has recently been effectively used to improve the balance of existing game levels. This approach, however, only assesses balance heuristically, neglecting actual human perception. For this reason, this work presents a survey to empirically evaluate the created content paired with human playtesting. Participants in four different scenarios are asked about their perception of changes made to the level both before and after balancing, and vice versa. Based on descriptive and statistical analysis, our findings indicate that the PCGRL-based balancing positively influences players' perceived balance for most scenarios, albeit with differences in aspects of the balancing between scenarios.

翻译：实现游戏的最佳平衡性对其成功至关重要，但这依赖于大量人工工作和游戏测试。为促进这一过程，基于强化学习的程序化内容生成（PCGRL）框架近期被有效用于改进现有游戏关卡的平衡性。然而，该方法仅通过启发式方式评估平衡性，忽略了实际的人类感知。为此，本研究通过实证调查结合人工游戏测试来评估生成内容。在四种不同场景中，参与者被询问关于关卡在平衡调整前后（以及反向调整）的感知变化。基于描述性统计与分析，我们的研究结果表明：尽管不同场景间的平衡调整维度存在差异，但基于PCGRL的平衡方法在多数场景中对玩家感知的平衡性产生了积极影响。