Reversely Discovering and Modifying Properties Based on Active Deep Q-Learning

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.

Abstract licence: CC BY-NC-ND

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