测试视频游戏是制作过程中的关键步骤，需要花费大量时间和资源。一些软件公司正试图使用人工智能来替代能够使用人工代理的系统来减少对人力资源的需求。我们研究了使用深度强化学习在三消游戏中自动化测试过程的可能性，并建议在“决斗深度Q网络”范式的框架内解决该问题。我们在Jelly Juice游戏（由redBit Games开发的Match-3视频游戏）上测试了这种网络。网络从游戏环境中提取基本信息，并推断下一步行动。我们将结果与随机播放器的性能进行比较，发现该网络显示出最高的成功率。
原文标题：Testing match-3 video games with Deep Reinforcement Learning
原文：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.
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