Research on dynamic team formation for multirobot based on reinforcement learning
Xingce Wang, Rubo Zhang, Guochang Gu · 2003
In the field of the artificial intelligence, more and more attention has been paid to the reinforcement learning algorithm with the advantage of its self-learning and self-adaptability. With the development of the multiagent theory in distributed artificial intelligence, the distributed reinforcement learning is becoming the focus of this research. A model of the multirobots' team formation is used as the study model to illuminate the high-level behavior control of the robots with the usage of the reinforcement learning. Now, few people apply this way to solve such problem. In the reinforcement learning algorithm explained here, the inside reinforcement signals and outside reinforcement signals are applied to show the interests of the robot and its whole group. The control system of the robot is composed of the high-level behavior control and the low-level action control. With this multilayer control, the task of every part is clear. In the low-level action control, the fuzzy control is used here for the mechanical character of the robot. After using the multilayer architecture and fuzzy control algorithm, the speed of learning and convergence of the reinforcement is faster.