论文标题
使用RL刷的混合量化水平设计
Mixed-Initiative Level Design with RL Brush
论文作者
论文摘要
本文介绍了RL Brush，这是一种用于基于瓷砖的游戏的级别编辑工具，专为混合启动性共同创建而设计。该工具使用基于加强学习的模型通过添加AI生成的建议来增强人工水平设计。在这里，我们将RL刷应用于设计经典益智游戏索科班的水平。我们将该工具放在线，并在39个不同的课程中对其进行了测试。结果表明，使用AI建议的用户持续更长的时间，并且平均而言，其创建的水平比不用的用户更具可玩性和更为复杂。
This paper introduces RL Brush, a level-editing tool for tile-based games designed for mixed-initiative co-creation. The tool uses reinforcement-learning-based models to augment manual human level-design through the addition of AI-generated suggestions. Here, we apply RL Brush to designing levels for the classic puzzle game Sokoban. We put the tool online and tested it in 39 different sessions. The results show that users using the AI suggestions stay around longer and their created levels on average are more playable and more complex than without.
