提供使用约束可解性查询的3D障碍赛道游戏快速设计反馈
Providing Rapid Design Feedback for 3D Obstacle Course Games Using Constrained Solvability Queries
- Stanford University（斯坦福大学）
- Roblox（罗布乐思）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出一个系统，利用约束可解性查询和强化学习智能体，为3D障碍赛道游戏设计师提供快速反馈，帮助理解障碍解法并探索新设计方向，经人类测试验证有效。
AI中文摘要：
我们提出一个系统，通过为设计师提供关于障碍如何被解决的快速反馈，来辅助3D障碍赛道游戏的设计。我们的核心贡献是一个查询系统，用于查询符合设计师指定约束（例如，避开某个区域、经过给定路径点、仅执行两次跳跃等）的障碍解决方案（玩家动作序列）。为了快速解决广泛的障碍设计，我们编写了GoExplore算法的高性能实现用于探索性搜索，并使用通过强化学习（RL）离线训练的障碍解决智能体来引导搜索。为了进一步加速搜索，系统使用自定义的GPU加速障碍赛道游戏模拟器进行探索，该模拟器以接近14,000倍实时速度生成游戏体验，即60帧每秒的游戏玩法。通过设计研究，我们证明了在快速设计循环中使用约束可解性查询具有足够的表达能力，可以帮助设计师理解障碍可以被解决的方式或为何无法被解决。我们还展示了该工具如何让设计师回答更高层次的问题，例如识别不理想的解决方案路径和评估解决方案的难度。这使他们能够探索原本未预料到的新设计方向。使用我们系统设计的障碍进行的人类游戏测试证实，人类玩家确实以设计师预期的方式游玩这些障碍。我们在以下网址发布了我们的交互式工具、模拟器、训练设置和程序化关卡生成系统的代码：此https URL。
英文摘要：
We present a system that aids the design of 3D obstacle course games by providing designers with rapid feedback on how obstacles can be solved. Our core contribution is a system for querying for solutions (sequences of player actions) to an obstacle that adhere to designer-specified constraints (e.g., avoid a region, travel through a given waypoint, only perform two jumps, etc.). To solve a wide range of obstacle designs quickly, we author a high-performance implementation of the GoExplore algorithm for exploratory search, and guide search with an obstacle solving agent trained offline using reinforcement learning (RL). To further accelerate search, the system carries out exploration using a custom GPU-accelerated obstacle course game simulator that generates playthrough experience at nearly 14,000$\times$ real time, 60-fps gameplay. Through design studies, we demonstrate that the use of constrained solvability queries in a rapid design loop is sufficiently expressive to help designers understand ways an obstacle can be solved or why it cannot be solved. We also show how the tool can let designers answer higher-level questions such as identifying undesirable solution paths and assessing the difficulty of solutions. This allows them to pursue new design directions they did not originally anticipate. Human playtesting of obstacles designed using our system confirms that human players indeed play the obstacles in the manner the designers intended. We release code for our interactive tool, simulator, training setup, and procedural level generation system at https://zandermajercik.github.io/interactive-obstacle-course-feedback/.
