Creating Human-like Autonomous Players in Real-time First Person Shooter Computer Games
Di Wang, Budhitama Subagdja, Ah‐Hwee Tan, Gee-Wah Ng · 2009
This paper illustrates how we create a software agent by em-ploying FALCON, a self-organizing neural network that per-forms reinforcement learning, to play a well-known first per-son shooter computer game known as Unreal Tournament 2004. Through interacting with the game environment and its opponents, our agent learns in real-time without any human intervention. Our agent bot participated in the 2K Bot Prize competition, similar to the Turing test for intelligent agents, wherein human judges were tasked to identify whether their opponents in the game were human players or virtual agents. To perform well in the competition, an agent must act like hu-man and be able to adapt to some changes made to the game. Although our agent did not emerge top in terms of human-like, the overall performance of our agent was encouraging as it acquired the highest game score while staying convinc-ing to be human-like in some judges ’ opinions.