Abstract: A method of guaranteeing game quality by using an artificial intelligence (AI) agent is provided. The method includes extracting an item list (hereinafter referred to as an inspection item list) for inspecting quality of a target game, extracting and storing log data corresponding to a test performance result for each item of the inspection item list, performing imitation learning of an AI agent model on the basis of the stored log data, performing an automatic test for inspecting quality of the target game by using the AI agent model on which the imitation learning is completed, and automatically recording a bug and an error detected by the AI agent model.

Abstract: A method of guaranteeing game quality by using an artificial intelligence (AI) agent is provided. The method includes extracting an item list (hereinafter referred to as an inspection item list) for inspecting quality of a target game, extracting and storing log data corresponding to a test performance result for each item of the inspection item list, performing imitation learning of an AI agent model on the basis of the stored log data, performing an automatic test for inspecting quality of the target game by using the AI agent model on which the imitation learning is completed, and automatically recording a bug and an error detected by the AI agent model.

Abstract: Device for simulation environment for training Al agent includes scene object providing module to provide scene and object in virtual content converted from original content; a reward function providing module to provide reward function used by agent to perform reinforcement learning in the virtual content; an environment information providing module to provide virtual environment information including information on environment where the agent performs the reinforcement learning in the virtual content; a status information providing module to provide virtual status information indicating status of the agent in the virtual content; an action space providing module to provide virtual action space indicating action of the agent; and a virtual learning module to create simulation environment based on at least one of the scene, the object, the reward function, the virtual environment information, the virtual status information, and the virtual action space, and perform virtual learning for the agent in the simula