关键词: 多Agent系统, 强化学习, Q-学习, 任务场景

Abstract: From the study of layered reinforcement learning approach，this paper proposes layered collaboration research methods through the mission structure analysis in MAS.By distinguishing the sub-mission，the state space is built，which is in greater level of granularity and based on the mission effect，combining with the mission action based on joint action and potential field model so as to solve the dimensional disaster in state space of reinforcement learning.This paper provides application of the algorithm based on a subset of mission in robot soccer and its effectiveness is validated by experiments.

Key words: Multi-Agent System（MAS）, reinforcement learning, Q-learning, mission scenarios