AUTOMATED VIDEO GAME TESTING USING REINFORCEMENT LEARNING AGENTS

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Date

2022-9-21

Author

Arıyürek, Sinan

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In this thesis, several methodologies are introduced to automate and improve video game playtesting. These methods are based on Reinforcement Learning (RL) agents. First, synthetic and human-like tester agents are proposed to automate video game testing. The synthetic agent uses test goals generated from game scenarios, and the human-like agent uses test goals extracted from tester trajectories. Tester agents are derived from Sarsa and Monte Carlo Tree Search (MCTS) but focus on finding defects, while traditional game-playing agents focus on maximizing game scores. Second, various MCTS modifications are proposed to enhance the bug-finding capabilities of MCTS. Third, to improve playtesting \textit{developing persona} is introduced, enabling development in an agent's personality and more robust playtesting. RL algorithms aim to find paths that maximize the total accumulated reward. However, discovering various alternative paths that achieve the same objective is equally essential for playtesting. Consequently, Alternative Path Finder (APF) is introduced to let RL agents discover these alternative paths. We experiment with our proposed methodologies using the General Video Game Artificial Intelligence (GVG-AI) and VizDoom frameworks. The experiments reveal that human-like and synthetic agents compete with human testers' bug-finding performances. Furthermore, the experiments indicate that MCTS modifications improve bug-finding performance. Moreover, the experiments show that developing personas provide better insight into the game and how different players would play. Lastly, the alternative paths found by APF are presented and reasoned why traditional RL agents cannot discover those paths.

Subject Keywords

Automated Game Testing, Automated Playtesting, Reinforcement Learning, Monte Carlo Tree Search, Player Modeling, Graph Coverage

URI

https://hdl.handle.net/11511/99066

Collections

Graduate School of Informatics, Thesis

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Citation Formats

S. Arıyürek, “AUTOMATED VIDEO GAME TESTING USING REINFORCEMENT LEARNING AGENTS,” Ph.D. - Doctoral Program, Middle East Technical University, 2022.