Simultaneous use of imitation learning and reinforcement learning in artificial intelligence development for video games

Abstract

The development of artificial intelligence is one of the most common problems in the video games industry. In most cases, the behavior of computer characters is defined by classical, deterministic algorithms. However, with increasing com-plexity of AI behavior, the complexity of the code describing that behavior also increases. Even more difficult is to create an AI that behaves like a real player. A deterministic algorithm will work efficiently but its behavior may look unnatural and unpleasant. To solve this problem, it is possible to use Machine Learning. Achievements in this field have found application in different niches, and video games have been no exception. This study explores the creation of a convincing and effective AI by simultaneous use of Imitation Learning and Reinforcement Learning. Also this study explores the tools for creating the Learning Environ-ment and learning AI agents, the principles of writing the program code for AI agents, and gives practical recommendations to speed up the learning of AI and improve its efficiency. As a practical example, for the purposes of this study, will be created an agent to control the tank, capable of maneuvering and fighting with several opponents simultaneously