论文标题
通过深度强化学习测试Match-3视频游戏
Testing match-3 video games with Deep Reinforcement Learning
论文作者
论文摘要
测试视频游戏是生产过程的关键步骤，需要在时间和资源方面做出巨大的努力。一些软件房屋正试图使用人工智能使用能够替代人类代理的系统来减少人力资源的需求。我们研究了使用深度强化学习来在Match-3视频游戏中自动化测试过程的可能性，并建议在决斗深Q-Network范式的框架中解决问题。我们在果冻果汁游戏上测试了这种网络，这是由Redbit Games开发的Match-3视频游戏。网络从游戏环境中提取基本信息，并在下一步移动。我们将结果与随机播放器的性能进行比较，发现网络显示出最高的成功率。在大多数情况下，结果与真实用户获得的结果相似，并且网络还成功地学习了随着时间的推移，可以区分游戏水平并将其策略改编到日益增长的困难的不同功能。
Testing a video game is a critical step for the production process and requires a great effort in terms of time and resources spent. Some software houses are trying to use the artificial intelligence to reduce the need of human resources using systems able to replace a human agent. We study the possibility to use the Deep Reinforcement Learning to automate the testing process in match-3 video games and suggest to approach the problem in the framework of a Dueling Deep Q-Network paradigm. We test this kind of network on the Jelly Juice game, a match-3 video game developed by the redBit Games. The network extracts the essential information from the game environment and infers the next move. We compare the results with the random player performance, finding that the network shows a highest success rate. The results are in most cases similar with those obtained by real users, and the network also succeeds in learning over time the different features that distinguish the game levels and adapts its strategy to the increasing difficulties.
