Augmenting Automated Game Testing with Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2103.15819v1
• Date: Mon, 29 Mar 2021 11:55:15 GMT
• Title: Augmenting Automated Game Testing with Deep Reinforcement Learning
• Authors: Joakim Bergdahl, Camilo Gordillo, Konrad Tollmar, Linus Gissl\'en
• Abstract summary: 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. We introduce a self-learning mechanism to the game testing framework using deep reinforcement learning (DRL) 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.
• Score: 0.4129225533930966
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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