A reinforcement self-learning model on an intelligent behavior avatar in a virtual world

In this paper, a novel method for personal intelligent behavior avatar (IBA) is proposed to acquire autonomous behavior based on the interactions between user and smart objects in the virtual environment. In this method, the behavior decision model and the self-learning model are integrated by Bayesian networks and reinforcement learning. The Bayesian networks can treat interaction experiences using statistical processes, and the sureness of decision making is represented by certainty factors using stochastic reasoning. The reinforcement learning is implemented by learning experimentation or trial and error mechanisms to improve the performance of IBA through feedback. Therefore, the IBA makes a strategic decision that is approximated and appropriate to the user through the self-learning process by reinforcement learning. Finally, the feasibility of this method is investigated by imitating user´s behavior and the results of self-learning process. The results of simulation show that the method is successful in imitating user´s behavior and improving the performance of IBA