论文标题
学习可控的3D级生成器
Learning Controllable 3D Level Generators
论文作者
论文摘要
通过强化学习（PCGRL）的程序内容生成（PCGRL）预示了对大型人为实现的数据集的需求，并允许代理使用可计算的，用户定义的质量量度而不是目标输出来明确训练功能约束。我们探讨了PCGRL在3D域中的应用，其中内容产生任务自然具有更大的复杂性和与现实世界应用的潜在相关性。在这里，我们介绍了3D域的几个PCGRL任务，Minecraft（Mojang Studios，2009年）。这些任务将使用经常在3D环境中发现的能力（例如跳跃，多维运动和重力）来挑战基于RL的发电机。我们培训一个代理，以优化这些任务中的每一个，以探索PCGRL先前研究的功能。该代理能够生成相对复杂和不同的水平，并推广到随机的初始状态和控制目标。提出的任务中的可控性测试证明了他们分析3D发电机成功和失败的实用性。
Procedural Content Generation via Reinforcement Learning (PCGRL) foregoes the need for large human-authored data-sets and allows agents to train explicitly on functional constraints, using computable, user-defined measures of quality instead of target output. We explore the application of PCGRL to 3D domains, in which content-generation tasks naturally have greater complexity and potential pertinence to real-world applications. Here, we introduce several PCGRL tasks for the 3D domain, Minecraft (Mojang Studios, 2009). These tasks will challenge RL-based generators using affordances often found in 3D environments, such as jumping, multiple dimensional movement, and gravity. We train an agent to optimize each of these tasks to explore the capabilities of previous research in PCGRL. This agent is able to generate relatively complex and diverse levels, and generalize to random initial states and control targets. Controllability tests in the presented tasks demonstrate their utility to analyze success and failure for 3D generators.
