用强化学习自动设计对称能力的关卡，让不同玩家类型胜率均等。
Level the Level: Balancing Game Levels for Asymmetric Player Archetypes With Reinforcement Learning
- 基于强化学习生成关卡，通过地形布局平衡不对称角色能力
- 在4类角色上验证，比基线方法平衡更多关卡
- 角色差距越大，训练越难，平衡效果越差
游戏平衡，尤其是包含非对称多人内容的游戏，需要大量人工投入和反复人工测试。为此，本文聚焦于为非对称玩家类型生成平衡的关卡，使能力差异完全通过关卡设计来调节。例如，尽管某一角色具有优势，但双方获胜概率应相等。我们将游戏平衡视为程序化内容生成问题，并改进和扩展了一种近期提出的基于强化学习的瓦片式关卡平衡方法。我们在四种不同玩家类型上评估该方法，结果表明其相比两种基线方法能平衡更多关卡。此外，实验显示，随着玩家类型间能力差距增大，所需训练步数增加，模型达到平衡的准确率下降。
原文摘要 · Abstract (English)
Balancing games, especially those with asymmetric multiplayer content, requires significant manual effort and extensive human playtesting during development. For this reason, this work focuses on generating balanced levels tailored to asymmetric player archetypes, where the disparity in abilities is balanced entirely through the level design. For instance, while one archetype may have an advantage over another, both should have an equal chance of winning. We therefore conceptualize game balancing as a procedural content generation problem and build on and extend a recently introduced method that uses reinforcement learning to balance tile-based game levels. We evaluate the method on four different player archetypes and demonstrate its ability to balance a larger proportion of levels compared to two baseline approaches. Furthermore, our results indicate that as the disparity between player archetypes increases, the required number of training steps grows, while the model's accuracy in achieving balance decreases.
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