  General game testing relies on the use of human play testers, play test
scripting, and prior knowledge of areas of interest to produce relevant test
data. Using deep reinforcement learning (DRL), we introduce a self-learning
mechanism to the game testing framework. With DRL, the framework is capable of
exploring and/or exploiting the game mechanics based on a user-defined,
reinforcing reward signal. As a result, test coverage is increased and
unintended game play mechanics, exploits and bugs are discovered in a multitude
of game types. In this paper, we show that DRL can be used to increase test
coverage, find exploits, test map difficulty, and to detect common problems
that arise in the testing of first-person shooter (FPS) games.
