Towards Building Human-like Smart Agents in Modern 3D Video Games (Student Abstract)

摘要

In recent years, reinforcement learning has been widely applied in the field of games. However, most studies focus on assisting agents to achieve victory, with less attention paid to whether the agents exhibit human-like characteristics. In order to build human-like agents with high performance, we propose a method for learning the strategies of human players in modern three-dimensional video games. Our method utilizes a hierarchical framework, learning basic behaviors and intentions of human players at the lower level through imitation learning, and generalized policies at the high level through reinforcement learning. Compared with other existing methods, our method demonstrates significant advantages in learning human-like strategies in complex environments.