论文标题
CCPT：具有好奇条件的近端轨迹的自动游戏测试和验证
CCPT: Automatic Gameplay Testing and Validation with Curiosity-Conditioned Proximal Trajectories
论文作者
论文摘要
本文提出了一种新颖的深钢筋学习算法，以在复杂的3D导航环境中对游戏问题进行自动分析和检测。好奇心条件的近端轨迹（CCPT）方法结合了好奇心和模仿学习，以训练代理，以有条不紊地探索来自专家示范的已知轨迹的近端。我们展示了CCPT如何在此过程中探索复杂的环境，发现游戏问题并设计监督，并将其直接识别为游戏设计师。我们进一步证明了该算法在新颖的3D导航环境中的有效性，该导航环境反映了现代AAA视频游戏的复杂性。我们的结果表明，比Baselines方法更高的覆盖范围和发现漏洞，因此可以为游戏设计师自动识别游戏中的问题提供宝贵的工具。
This paper proposes a novel deep reinforcement learning algorithm to perform automatic analysis and detection of gameplay issues in complex 3D navigation environments. The Curiosity-Conditioned Proximal Trajectories (CCPT) method combines curiosity and imitation learning to train agents to methodically explore in the proximity of known trajectories derived from expert demonstrations. We show how CCPT can explore complex environments, discover gameplay issues and design oversights in the process, and recognize and highlight them directly to game designers. We further demonstrate the effectiveness of the algorithm in a novel 3D navigation environment which reflects the complexity of modern AAA video games. Our results show a higher level of coverage and bug discovery than baselines methods, and it hence can provide a valuable tool for game designers to identify issues in game design automatically.
