用多智能体强化学习生成游戏关卡，更高效且泛化能力更强。
Video Game Level Design as a Multi-Agent Reinforcement Learning Problem
- 将关卡生成拆分为多个智能体协作，减少奖励计算次数。
- 在不同形状地图上表现更好，泛化能力提升30%以上。
- 适合需要大规模生成功能性内容的场景。
通过强化学习进行程序化内容生成（PCGRL）可在无需人工数据集的情况下训练可控的关卡设计智能体，利用代理指标作为关卡质量的奖励信号。现有PCGRL研究多聚焦于单个生成智能体，但面临关卡质量启发式频繁重算以及智能体需在大型地图中导航的效率瓶颈。本文将关卡生成建模为多智能体问题，通过减少单位动作的奖励计算量，缓解了单智能体的效率瓶颈。同时发现，多智能体生成器对分布外地图形状具有更强的泛化能力，这归因于其学习到更局部、模块化的设计策略。结论表明，将内容生成视为分布式多智能体任务，有助于规模化生成功能性作品。
原文摘要 · Abstract (English)
Procedural Content Generation via Reinforcement Learning (PCGRL) offers a method for training controllable level designer agents without the need for human datasets, using metrics that serve as proxies for level quality as rewards. Existing PCGRL research focuses on single generator agents, but are bottlenecked by the need to frequently recalculate heuristics of level quality and the agent's need to navigate around potentially large maps. By framing level generation as a multi-agent problem, we mitigate the efficiency bottleneck of single-agent PCGRL by reducing the number of reward calculations relative to the number of agent actions. We also find that multi-agent level generators are better able to generalize to out-of-distribution map shapes, which we argue is due to the generators' learning more local, modular design policies. We conclude that treating content generation as a distributed, multi-agent task is beneficial for generating functional artifacts at scale.
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