论文标题
通过基于RL的人类游戏风格的生成自动游戏测试
Automated Play-Testing Through RL Based Human-Like Play-Styles Generation
论文作者
论文摘要
现代视频游戏中游戏机制的复杂性越来越复杂，导致出现了更广泛的游戏方式。设计师需要通过自动测试来预测各种可能的游戏风格。强化学习是对自动化视频游戏测试的需求的有前途的答案。为此，人们需要训练代理商玩游戏，同时确保该代理商将与玩家产生相同的游戏风格，以便向设计师提供有意义的反馈。我们提出CARMI：具有相对指标作为输入的可配置代理。即使在以前看不见的水平上，也能够模仿玩家玩游戏风格的经纪人。与当前的方法不同，它不依赖于具有完整的轨迹，而仅依赖摘要数据。此外，它只需要很少的人类数据，因此与现代视频游戏制作的限制兼容。这个新颖的代理商可用于调查在制作具有逼真培训时间的视频游戏过程中的行为和平衡。
The increasing complexity of gameplay mechanisms in modern video games is leading to the emergence of a wider range of ways to play games. The variety of possible play-styles needs to be anticipated by designers, through automated tests. Reinforcement Learning is a promising answer to the need of automating video game testing. To that effect one needs to train an agent to play the game, while ensuring this agent will generate the same play-styles as the players in order to give meaningful feedback to the designers. We present CARMI: a Configurable Agent with Relative Metrics as Input. An agent able to emulate the players play-styles, even on previously unseen levels. Unlike current methods it does not rely on having full trajectories, but only summary data. Moreover it only requires little human data, thus compatible with the constraints of modern video game production. This novel agent could be used to investigate behaviors and balancing during the production of a video game with a realistic amount of training time.
