A Reinforcement Learning Behavior Tree Framework for Game AI
Yanchang Fu, Long Qin, Quanjun Yin · 2016
This paper discussed the implementation of behavior tree technology in behavioral modeling domain.Existing framework can't provide the ability of reasoning while take into account the ability of learning.To solve this problem, we propose a reinforcement learning behavior tree framework based on reinforcement theory.Following our study, a QBot model is build based on the framework in the Raven platform, a popular test bed for game AI development.This paper carried out simulation experiments which include 3 opponent agents.The result shows that QBot outperforms the other 2 Raven_Bots which adopt the default agent model in Raven platform, and thus the result proves that the framework is advanced.