论文标题
用于自动化可及性测试的Go-explore复杂3D游戏环境
Go-Explore Complex 3D Game Environments for Automated Reachability Testing
论文作者
论文摘要
现代AAA视频游戏具有庞大的游戏水平和地图，越来越难以详尽的测试人员覆盖。结果，游戏经常带着灾难性的虫子发货，例如玩家落在地板上或卡在墙壁上。我们提出了一种基于强大的探索算法，Go-explore的模拟3D环境中针对可及性错误的方法，该方法在地图上保存了独特的检查点，然后确定有希望的探索。我们表明，当Go-explore与从游戏导航网格中得出的简单启发式方法相结合时，发现了具有挑战性的错误，并全面探索了复杂的环境，而无需人类演示或游戏动力学知识。探索大大胜过更复杂的基线，包括增强学习，并在涵盖导航网格和发现的整个地图上的独特位置数量中都具有内在好奇心。最后，由于我们使用并行代理，我们的算法可以在10小时内在10小时内完全覆盖1.5公里x 1.5公里的游戏世界，这对于连续测试套件非常有希望。
Modern AAA video games feature huge game levels and maps which are increasingly hard for level testers to cover exhaustively. As a result, games often ship with catastrophic bugs such as the player falling through the floor or being stuck in walls. We propose an approach specifically targeted at reachability bugs in simulated 3D environments based on the powerful exploration algorithm, Go-Explore, which saves unique checkpoints across the map and then identifies promising ones to explore from. We show that when coupled with simple heuristics derived from the game's navigation mesh, Go-Explore finds challenging bugs and comprehensively explores complex environments without the need for human demonstration or knowledge of the game dynamics. Go-Explore vastly outperforms more complicated baselines including reinforcement learning with intrinsic curiosity in both covering the navigation mesh and number of unique positions across the map discovered. Finally, due to our use of parallel agents, our algorithm can fully cover a vast 1.5km x 1.5km game world within 10 hours on a single machine making it extremely promising for continuous testing suites.
