Path-Restricted Parallel Q-Learning Algorithm in Collaborative Virtual Environment

In order to improve the application effect of the collaborative navigation control, this paper presents a Q-learning algorithm based on the path restriction by constructing the absolute distance between a mobile agent of the virtual environment and its destination into a status function of reinforcement learning. In comparison with late and former statuses, a shortest path usually can be achieved. At the same time, the results of the learning can be shared by other agents, which can strengthen their perception of environmental information, learn the right decision-making more quickly, and make efficient route-seeking and navigation control.