关键词: 多Agent系统, 协作, 量子计算, Q-学习, 均衡解, 蚁群算法

Abstract: Due to the interactions among the Agents in the cooperative multi-Agent systems，multi-Agent learning problem complexity can rise rapidly with the number of Agents or their behavioral sophistication.In order to converge to desirable equilibrium，Agents generally require sufficient exploration of strategy space and coordinate their policies to achieve optimal equilibrium.A novel cooperative multi-Agent learning method is proposed based on quantum theory，ant algorithm and Q-learning.First，this method not only coordinates Agents’ behaviors using quantum entanglement and helps Agents make action selection under quantum superposition，but also adopts Grover’s searching algorithm which can probe the action，speed up learning.Second，according to ant algorithm，footmark thought is presented so that Agents can be indirectly enforced to communicate with others.At last，the theory analysis and result of experiment both demonstrate that the improved Q-learning is feasible and increases the learning efficiency.

Key words: multi-Agent system, cooperative, quantum computing, Q-learning, equilibrium, ant colony algorithm