With the more robots, the information interaction of multi-robot system becomes more sophisticated and important in a community perception network environment. By exploiting and fusing the learning information of robots in a perception community, the community information sharing mechanism is proposed, as well as updating rules of the community Q-value table. Moreover, considering the existence of delays of learning information transmission, an improved Q-learning method based on homogeneous delays is presented to improve the robot learning efficiency over the community perception network. Finally, the test experiments demonstrate the effectiveness of the proposed scheme.
Enhanced robot learning using fuzzy Q-Learning & context-aware middleware
