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.
翻译:现代 AAA 视频游戏具有巨大的游戏水平和地图,对于水平测试者来说,它们越来越难以全面覆盖。 结果,游戏往往以灾难性的错误(如玩家倒在地板上或被困在墙壁中)进行,我们建议一种基于强大的探索算法Go-Explore(Go-Explore)的具体针对模拟的3D环境中的可达性错误的方法,它节省了地图上独特的检查站,然后又确定了可以探索的。我们显示,当与游戏导航网目产生的简单超常游戏相结合时,Go-Explore发现有挑战性的错误并全面探索复杂的环境,而不需要人类演示或了解游戏动态。 Go-Explore (Go-Explore) 大大超越了更复杂的基线, 包括用内在的好奇心加强学习, 覆盖在地图上发现的导航网格和不同位置的数量。 最后,由于我们使用平行工具, 我们的算法可以在10小时内完全覆盖一个巨大的1.5km x 1.5 km 5 km 游戏世界。