Multi-Agent Q-learning based on quantum theory and ant colony algorithm
Wang Sheng-bin · Computer Engineering and Applications Journal · 2010
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 learn-ing.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.