Research on the integrated application of machine learning in Unity

Abstract. In recent years, the introduction of Machine Learning Agents has enabled more individuals to apply machine learning within the renowned Unity game engine. As application development has become increasingly complex, traditional artificial intelligence (AI) struggles to keep pace with the growing demands of modern applications. However, machine learning offers new avenues for developing smarter, more dynamic game characters. This paper aims to provide a comprehensive review of the various applications of machine learning within the Unity ecosystem, including game AI, intelligent character behavior, and reinforcement learning. Through a literature review and case studies, this paper explores the specific applications of machine learning algorithms in the Unity engine and analyzes the current state of development in different fields. The research findings indicate that the application of machine learning in Unity exhibits numerous developmental trends and leaves ample room for further application development in its strong areas. This technology can be innovatively applied across many fields, from implementing advanced NPCs and third-person game agents to anti-cheat mechanisms, from image recognition-based health systems to the design of complex constrained environments. The integrated application model of machine learning and Unity3D will bring more innovation and possibilities to future application development.