A Task Distribution Based Q-Learning Algorithm for Multi- Agent Team Coordination
Qiao Sun1, Zhibo Chen1, Feixiang Chen1, Fu Xu1, Yanan Shi2
COMPUTER MODELLING & NEW TECHNOLOGIES 2014 18(12C) 736-740
1 School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
2 College of Computer Science and Technology, Jilin University, Changchun 130012, China
It is difficult to apply traditional Q-learning algorithm to Multi-Agent environment, because in this case, the size of state-action space is so huge that it is hard to obtain the global optimal solution. In the paper, a task distribution based Q-learning algorithm is proposed to solve this problem. In this algorithm, at each learning step, it first distributes sub-task to each Agent dynamically. The Learning processes include the learning of task-distribution strategy and the learning of action-selection strategy synchronously, and every Agent shares the Q value table. Both Theoretical analysis and experimental results demonstrate that the proposed algorithm outperforms conventional Q-learning algorithm.