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Reversely Discovering and Modifying Properties Based on Active Deep Q-Learning
2020
IEEE Access
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
doi:10.1109/access.2020.3019278
fatcat:vbcdj2zsnzaxpcbu3uae2awz4e