Many researchers studied DQN (Deep Q-Networks) to train a game AI to beat human players, while we trained an improved AI to reversely modify properties of 3D video games. Our ultimate objective is to improve automatic debug for software and cloud services. However, the problem that reversely discovers properties in online 3D Video Games in an automatic way has not been studied yet. Therefore, related special difficulties are first discussed in the paper. RMDQN (a Reverse Method based on our active Deep Q-Networks) is proposed to deal with the problem, and an active DQN is invented to make the reverse procedure automatic and intelligent. The action engine of RMDQN is able to control any operational game object like a player is playing, which makes automatic debug possible. A video demonstration is provided to show the result of reversely modifying game properties by our method. It was proved that our method can improve debug technology in 3D video games, and it will be applied in cloud services with few modifications.

中文翻译：

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输配电的前八年——1873年——1880年【回顾我们的过去】


许多研究人员研究 DQN（深度 Q 网络）来训练游戏 AI 来击败人类玩家，而我们则训练改进的 AI 来反向修改 3D 视频游戏的属性。我们的最终目标是改进软件和云服务的自动调试。然而，以自动方式反向发现在线3D视频游戏中的属性的问题尚未被研究。因此，本文首先讨论了相关的特殊困难。 RMDQN（一种基于我们的主动深度Q网络的逆向方法）被提出来处理这个问题，并发明了主动DQN来使逆向过程自动化和智能化。 RMDQN的动作引擎能够像玩家玩游戏一样控制任何可操作的游戏对象，这使得自动调试成为可能。提供了视频演示来展示我们的方法反向修改游戏属性的结果。事实证明，我们的方法可以改进3D视频游戏中的调试技术，并且只需少量修改即可应用于云服务。